Differences in Clinical and Dietary Characteristics, Serum Adipokine Levels, and Metabolomic Profiles between Early- and Late-Onset Gout.
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ABSTRACT:
SUBMITTER: Suh YS
PROVIDER: S-EPMC8234189 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:A retrospective cross-sectional study using data from an outpatient clinic in China was conducted to investigate the clinical features of early-onset gout patients.All patients diagnosed with gout were asked about clinical characteristics of their gout and comorbid diseases. Patients presenting with acute flares were asked about common triggers before the flare. "Early-onset" gout was defined as onset of gout before 40 years and "late-onset" as onset ≥40 years. Major joint involvement, flare frequency before presentation, the cumulative number of involved joints, proportions of tophi complications at presentation, flare triggers, as well as any metabolic, cardiovascular, cerebrovascular, and renal comorbidities, were compared between the 2 groups.A total of 778 gout patients were enrolled in this study, including 449 (57.7%) in the early-onset group and 329 (42.3%) in the late-onset group. Compared with the late-onset gout patients, the early-onset gout patients had a higher proportion of ankle/mid-foot involvement (62.8% vs 48.2%, P < 0.001), more frequent flares before presentation (11.2 ± 1.17 vs 6.97 ± 1.03 times per year, P = 0.01), higher cumulative number of involved joints (5.2 ± 0.26 vs 3.8 ± 0.26, P < 0.001), and more likely to have alcohol consumption as a flare trigger (65.2% vs 53.9%, P = 0.03); whereas early-onset gout patients had fewer metabolic, cardiovascular, cerebrovascular, or renal complications.Early- and late-onset gout patients had different clinical features. Early-onset seems to be influenced more by lifestyle, while late-onset patients have more complications because of comorbidities.
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Project description:The food-based empirical dietary index for hyperinsulinemia (EDIH) score assesses the insulinemic potential of diet. This cross-sectional study evaluated associations between EDIH scores from food frequency questionnaires with c-peptide concentrations and with 448 metabolites, from fasting plasma samples, in multivariable linear regression analyses. Metabolites were measured with liquid chromatography tandem mass spectroscopy. Using a robust two-stage study design, discovery of metabolite associations was conducted among 1109 Women's Health Initiative (WHI) Hormone Therapy (HT) trial participants and results replicated in an independent dataset of 810 WHI Observational Study (OS) participants. In both discovery and replication datasets, statistical significance was based on the false-discovery rate adjusted P < 0.05. In the multivariable-adjusted analyses, EDIH was significantly associated with c-peptide concentrations among 919 women (HT & OS) with c-peptide data. On average, c-peptide concentrations were 18% higher (95% CI, 6%, 32%; P-trend < 0.0001) in EDIH quintile 5 compared to quintile 1. Twenty-six metabolites were significantly associated with EDIH in the discovery dataset, and 19 of these were replicated in the validation dataset. Nine metabolites were found to decrease in abundance with increasing EDIH scores and included: C14:0 CE, C16:1 CE, C18:1 CE, C18:3 CE, C20:3 CE, C20:5 CE, C36:1 PS plasmalogen, trigonelline, and eicosapentanoate, whereas the 10 metabolites observed to increase with increasing EDIH scores were: C18:2 SM, C36:3 DAG, C36:4 DAG-A, C51:3 TAG, C52:3 TAG, C52:4, TAG, C54:3 TAG, C54:4 TAG, C54:6 TAG, and C10:2 carnitine. Cholesteryl esters, phospholipids, acylglycerols, and acylcarnitines may constitute circulating metabolites that are associated with insulinemic dietary patterns.
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Project description:ObjectiveAcute-onset diffuse interstitial lung disease (AoDILD) includes acute exacerbation of interstitial lung disease (ILD), drug-induced ILD, and Pneumocystis pneumonia, and frequently occurs in patients with rheumatoid arthritis (RA). Since AoDILD causes a poor prognosis in RA, biomarkers for AoDILD were eagerly desired. Metabolomic analyses were extensively performed in cancer patients and successfully generated better diagnostic biomarkers. In the present study, serum metabolomic profiles of AoDILD in RA were investigated to generate better potential metabolomic biomarkers.MethodsSerum samples of 10 RA patients with AoDILD were collected on admission and in a stable state, more than 3 months before the admission. Serum metabolomic analyses were conducted on the samples from these RA patients with AoDILD.ResultsApparently distinct serum metabolomic profiles in AoDILD were not observed in univariate or hierarchical cluster analyses. Partial least squares-discriminant analysis (PLS-DA) was performed to select candidate metabolites based on variable importance in projection (VIP) scores. The PLS-DA model generated from the four metabolites with VIP scores more than 2.25 (mannosamine, alliin, kynurenine, and 2-hydroxybutyric acid) could successfully discriminate AoDILD from the stable condition (area under the curve: 0.962, 95% confidence interval: 0.778-1.000).ConclusionIt was demonstrated that metabolomic profiling was useful to generate better biomarkers in AoDILD.
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Project description:BackgroundNecrotising enterocolitis (NEC) and late-onset sepsis (LOS) are the leading causes of death among preterm infants in the developed world. This study aimed to explore the serum proteome and metabolome longitudinally in preterm infants with NEC or LOS, matched to controls.MethodsNineteen patients (10 cases, 9 controls) were included. A sample 14 d prior to and following, as well as at disease diagnosis, was included for cases. Controls had serum matched at diagnosis for corresponding case. All samples (n = 39) underwent shotgun proteomic analysis, and 37 samples also underwent metabolomics analysis using ultra performance liquid chromatography-tandem mass spectrometry.ResultsThe proteomic and metabolomic profiles of serum were comparable between all infants. Eight proteins were associated with NEC and four proteins were associated with LOS. C-reactive protein was increased in all NEC patients at diagnosis.ConclusionNo single protein or metabolite was detected in all NEC or LOS cases which was absent from controls; however, several proteins were identified which were associated with disease status. The differing expression of these proteins between diseased infants potentially relates to differing pathophysiology of disease. Thus, it is unlikely a single biomarker exists for NEC and/or LOS.
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Project description:Background: Clinical nutrition research often lacks robust markers of compliance, complicating the interpretation of clinical trials and observational studies of free-living subjects.Objective: We aimed to examine metabolomics profiles in response to 3 diets that differed widely in macronutrient composition during a controlled feeding protocol.Design: Twenty-one adults with a high body mass index (in kg/m2; mean ± SD: 34.4 ± 4.9) were given hypocaloric diets to promote weight loss corresponding to 10-15% of initial body weight. They were then studied during weight stability while consuming 3 test diets, each for a 4-wk period according to a crossover design: low fat (60% carbohydrate, 20% fat, 20% protein), low glycemic index (40% carbohydrate, 40% fat, 20% protein), or very-low carbohydrate (10% carbohydrate, 60% fat, 30% protein). Plasma samples were obtained at baseline and at the end of each 4-wk period in the fasting state for metabolomics analysis by using liquid chromatography-tandem mass spectrometry. Statistical analyses included adjustment for multiple comparisons.Results: Of 333 metabolites, we identified 152 whose concentrations differed for ?1 diet compared with the others, including diacylglycerols and triacylglycerols, branched-chain amino acids, and markers reflecting metabolic status. Analysis of groups of related metabolites, with the use of either principal components or pathways, revealed coordinated metabolic changes affected by dietary composition, including pathways related to amino acid metabolism. We constructed a classifier using the metabolites that differed between diets and were able to correctly identify the test diet from metabolite profiles in 60 of 63 cases (>95% accuracy). Analyses also suggest differential effects by diet on numerous cardiometabolic disease risk factors.Conclusions: Metabolomic profiling may be used to assess compliance during clinical nutrition trials and the validity of dietary assessment in observational studies. In addition, this methodology may help elucidate mechanistic pathways linking diet to chronic disease risk. This trial was registered at clinicaltrials.gov as NCT00315354.
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Project description:This study evaluated the clinical use of serum metabolomics to discriminate malignant cancers including pancreatic cancer (PC) from malignant diseases, such as biliary tract cancer (BTC), intraductal papillary mucinous carcinoma (IPMC), and various benign pancreaticobiliary diseases. Capillary electrophoresismass spectrometry was used to analyze charged metabolites. We repeatedly analyzed serum samples (n = 41) of different storage durations to identify metabolites showing high quantitative reproducibility, and subsequently analyzed all samples (n = 140). Overall, 189 metabolites were quantified and 66 metabolites had a 20% coefficient of variation and, of these, 24 metabolites showed significant differences among control, benign, and malignant groups (p < 0.05; Steel-Dwass test). Four multiple logistic regression models (MLR) were developed and one MLR model clearly discriminated all disease patients from healthy controls with an area under receiver operating characteristic curve (AUC) of 0.970 (95% confidential interval (CI), 0.946-0.994, p < 0.0001). Another model to discriminate PC from BTC and IPMC yielded AUC = 0.831 (95% CI, 0.650-1.01, p = 0.0020) with higher accuracy compared with tumor markers including carcinoembryonic antigen (CEA), carbohydrate antigen 19-9 (CA19-9), pancreatic cancer-associated antigen (DUPAN2) and s-pancreas-1 antigen (SPAN1). Changes in metabolomic profiles might be used to screen for malignant cancers as well as to differentiate between PC and other malignant diseases.
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Project description:The incidence of Alzheimer's disease (AD) increases with age and is becoming a significant cause of worldwide morbidity and mortality. However, the metabolic perturbation behind the onset of AD remains unclear. In this study, we performed metabolite profiling in both brain (n = 109) and matching serum samples (n = 566) to identify differentially expressed metabolites and metabolic pathways associated with neuropathology and cognitive performance and to identify individuals at high risk of developing cognitive impairment. The abundances of 6 metabolites, glycolithocholate (GLCA), petroselinic acid, linoleic acid, myristic acid, palmitic acid, palmitoleic acid and the deoxycholate/cholate (DCA/CA) ratio, along with the dysregulation scores of 3 metabolic pathways, primary bile acid biosynthesis, fatty acid biosynthesis, and biosynthesis of unsaturated fatty acids showed significant differences across both brain and serum diagnostic groups (P-value < 0.05). Significant associations were observed between the levels of differential metabolites/pathways and cognitive performance, neurofibrillary tangles, and neuritic plaque burden. Metabolites abundances and personalized metabolic pathways scores were used to derive machine learning models, respectively, that could be used to differentiate cognitively impaired persons from those without cognitive impairment (median area under the receiver operating characteristic curve (AUC) = 0.772 for the metabolite level model; median AUC = 0.731 for the pathway level model). Utilizing these two models on the entire baseline control group, we identified those who experienced cognitive decline in the later years (AUC = 0.804, sensitivity = 0.722, specificity = 0.749 for the metabolite level model; AUC = 0.778, sensitivity = 0.633, specificity = 0.825 for the pathway level model) and demonstrated their pre-AD onset prediction potentials. Our study provides a proof-of-concept that it is possible to discriminate antecedent cognitive impairment in older adults before the onset of overt clinical symptoms using metabolomics. Our findings, if validated in future studies, could enable the earlier detection and intervention of cognitive impairment that may halt its progression.
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Project description:Background: Leigh syndrome (LS) is the most common pediatric mitochondrial diseases caused by MRC defect. LS patients typically have onset age before 2 years old and have various clinical features. The purpose of this study was to evaluate the various characteristics between the group that were early onset and late onset patients. Methods: The medical records of this study used records between 2006 and 2017 (N = 110). Clinical characteristics, diagnostic evaluations, and neuro image studying of LS were reviewed in our study. We statistically analyzed data from patients diagnosed with LS at our hospital by using subgroup analysis was performed to divide patients according to the onset age. Results: Among the patients, 89 patients (80.9%) had the onset age before 2 years old, and 21 patents (19.1%) had onset age after 2 years old. In subgroup analysis first clinical presentation age, diagnosis age and several onset symptoms in the clinical characteristics were statistically significant. Early onset age group showed delayed development and late onset age group showed motor weakness and ataxia. However, Diagnostics evaluation and MRI findings showed no significant differences. The clinical status monitored during the last visit showed statistically significant differences in the clinical severity. In the early onset age group clinical status was more severe than late onset age group. Conclusions: Although the onset of Leigh syndrome is known to be under 2 years, there are many late onset cases were existed more than expected. Early onset LS patients have poor prognosis compare with late onset LS patients. Therefore, the specific phenotype according to the age of onset should be well-observed. Onset of LS is important in predicting clinical severity or prognosis, and it is necessary to provide individualized treatment or follow-up protocols for each patient.
| S-EPMC7174756 | biostudies-literature
Project description:BackgroundEthnic disparities in metabolic disease risk may be the result of differences in circulating adipokines and inflammatory markers related to ethnic variations in obesity and body fat distribution.Subjects/methodsIn a cross-sectional design, we compared serum levels of leptin, adiponectin, C-reactive protein (CRP), interleukin-6 (IL-6) and tumor necrosis factor-α (TNF-α) in control subjects (321 men and 930 women) from two nested case-control studies conducted within the Multiethnic Cohort Study consisting of whites, Japanese Americans (JA), Latinos, African Americans (AA) and Native Hawaiians (NH). General linear models were applied to evaluate ethnic differences in log-transformed serum biomarker levels before and after adjusting for body mass index (BMI) at cohort entry.ResultsIn comparison to whites, significant ethnic differences were observed for all biomarkers except TNF-α. JA men and women had significantly lower leptin and CRP levels than whites, and JA women also had lower adiponectin levels. Leptin was significantly higher in AA women (P < 0.01), adiponectin was significantly lower in AA men and women (P = 0.02 and P < 0.001), and CRP and IL-6 were significantly higher in AA men and women. Lower adiponectin (P < 0.0001) and CRP (P = 0.03) levels were the only biomarkers in NH women that differed from whites; no statistically significant differences were seen for NH men and for Latino men and women. When adjusted for BMI at cohort entry, the differences between the lowest and the highest values across ethnic groups decreased for all biomarkers except adiponectin in men indicating that ethnic differences were partially due to weight status.ConclusionsThese findings demonstrate the ethnic variations in circulating adipokine and CRP levels before and after adjustment for BMI. Given the limitation of BMI as a general measure of obesity, further investigation with visceral and subcutaneous adiposity measures are warranted to elucidate ethnicity-related differences in adiposity in relation to disparities in obesity-related disease risk.
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Project description:Early- and late-onset narcolepsy constitutes two distinct diagnostic subgroups. However, it is not clear whether symptomology and genetic risk factors differ between early- and late-onset narcoleptics. This study compared clinical data and single-nucleotide polymorphisms (SNPs) between early- and late-onset patients in a large cohort of 899 Han Chinese narcolepsy patients. Blood, cerebrospinal fluid, and clinical data were prospectively collected from patients, and patients were genotyped for 40 previously reported narcolepsy risk-conferring SNPs. Genetic risk scores (GRSs), associations of five different sets of SNPs (GRS1-GRS5) with early- and late-onset narcolepsy, were evaluated using logistic regression and receiver operating characteristic curves. Mean sleep latency was significantly shorter in early-onset cases than in late-onset cases. Symptom severity was greater among late-onset patients, with higher rates of sleep paralysis, hypnagogic hallucinations, health-related quality of life impairment, and concurrent presentation with four or more symptoms. Hypocretin levels did not differ significantly between early- and late-onset cases. Only rs3181077 (CCR1/CCR3) and rs9274477 (HLA-DQB1) were more prevalent among early-onset cases. Only GRS1 (26 SNPs; OR = 1.513, 95% CI: 0.893-2.585; P < 0.05) and GRS5 (6 SNPs; OR = 1.893, 95% CI: 1.204-2.993; P < 0.05) were associated with early-onset narcolepsy, with areas under the receiver operating characteristic curves of 0.731 and 0.732, respectively. Neither GRS1 nor GRS5 included SNPs in HLA regions. Our results indicate that symptomology and genetic risk factors differ between early- and late-onset narcolepsy. This protocol was approved by the Institutional Review Board (IRB) Panels on Medical Human Subjects at Peking University People's Hospital, China (approval No. Yuanlunshenlinyi 86) in October 2011.
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