A large study of metabolomics reveals common and distinct metabolic biomarkers for type 2 diabetes, coronary heart disease, and stroke.

We aimed at examining the shared and unique associations of metabolites with multiple cardiometabolic diseases (CMD), i.e. type 2 diabetes (T2D), coronary heart disease (CHD) and stroke. In this study, a total of 168 plasma metabolites were measured by targeted high-throughput nuclear magnetic resonance spectroscopy among 98,162 participants free of T2D, CHD, and stroke at baseline. Cox proportional hazard models estimated hazard ratios for one SD increase in metabolite concentration levels, and false discovery rate (at 10%) was used to correct for multiple comparisons. Over 12.1 years of follow-up on average, 3,463 T2D, 6,186 CHD, and 1,892 stroke events were recorded. Most lipoprotein metabolites were associated with risks of T2D and CHD but not with the risk of stroke, with stronger associations for T2D than for CHD. Phospholipids within intermediate-density lipoprotein or large low-density lipoprotein particles showed positive associations with CHD and inverse associations with T2D. Metabolites indicating very small very low-density lipoprotein, histidine, creatinine, albumin, and glycoprotein acetyls were associated with risks of all three conditions. This large-scale metabolomics study revealed common and distinct metabolic biomarkers for T2D, CHD and stroke, providing instrumental information to possibly implement precision medicine for preventing and treating these conditions.

Semiparametric regression analysis of length-biased and partly interval-censored data with application to an AIDS cohort study.

Length-biased data occur often in many scientific fields, including clinical trials, epidemiology surveys and genome-wide association studies, and many methods have been proposed for their analysis under various situations. In this article, we consider the situation where one faces length-biased and partly interval-censored failure time data under the proportional hazards model, for which it does not seem to exist an established method. For the estimation, we propose an efficient nonparametric maximum likelihood method by incorporating the distribution information of the observed truncation times. For the implementation of the method, a flexible and stable EM algorithm via two-stage data augmentation is developed. By employing the empirical process theory, we establish the asymptotic properties of the resulting estimators. A simulation study conducted to assess the finite-sample performance of the proposed method suggests that it works well and is more efficient than the conditional likelihood approach. An application to an AIDS cohort study is also provided.

Handgrip strength is associated with risks of new-onset stroke and heart disease: results from 3 prospective cohorts.

BACKGROUND: Stroke and heart disease are two major contributors to the global disease burden. We aimed to evaluate and compare the roles of different handgrip strength (HGS) expressions in predicting stroke and heart disease in three nationally representative cohorts. METHODS: This longitudinal study used data from the Health and Retirement Study (HRS), the Survey of Health, Ageing, and Retirement in Europe (SHARE), and the China Health and Retirement Longitudinal Study (CHARLS). The Cox proportional hazard model was applied to analyze the relationship between HGS and stroke and heart disease, and Harrell's C index was used to assess the predictive abilities of different HGS expressions. RESULTS: A total of 4,407 participants suffered from stroke and 9,509 from heart disease during follow-up. Compared with the highest quartile, participants in the lowest quartile of dominant HGS, absolute HGS and relative HGS possessed a significantly higher risk of new-onset stroke in Europe, America, and China (all P < 0.05). After adding HGS to office-based risk factors, there were minimal or no differences in the increases of Harrell's C indexes among three HGS expressions. In contrast, the modest association between HGS and heart disease was only seen in SHARE and HRS, but not in CHARLS. CONCLUSION: Our findings support that HGS can be used as an independent predictor of stroke in middle-aged and older European, American and Chinese populations, and the predictive ability of HGS may not depend on how it is expressed. The relationship between HGS and heart disease calls for further validation.

Associations of handgrip strength with morbidity and all-cause mortality of cardiometabolic multimorbidity.

BACKGROUND: Cardiometabolic multimorbidity (CM) is an increasing public health and clinical concern. However, predictors for the development and prognosis of CM are poorly understood. The aims of this study were to investigate the relation between handgrip strength (HGS) and the risk of CM and to examine the association of HGS with all-cause mortality risk among patients with CM. METHODS: This prospective cohort study involved 493,774 participants from the UK Biobank. CM was defined as the simultaneous occurrence of two or more of the following conditions: type 2 diabetes, stroke, and coronary heart disease (CHD). Cox proportional hazards models were performed to estimate hazard ratios (HRs) and 95% confidence intervals (95% CIs). RESULTS: During a median follow-up of 12.1 years, 4701 incident CM cases were documented among participants with none cardiometabolic disease at baseline. Compared with the fourth quartile (Q4), the multivariable adjusted HR (95% CI) value of Q1 of HGS for developing CM was 1.46 (1.34-1.60). In participants with one cardiometabolic disease at baseline, participants in Q1 of HGS also possessed higher risk of CM than those in Q4, with HRs (95% CIs) being 1.35 (1.23-1.49) in patients with type 2 diabetes, 1.23 (1.04-1.46) in patients with stroke, and 1.23 (1.11-1.36) in patients with CHD. For participants with CM at recruitment, HGS was also associated with the risk of all-cause mortality (Q1 vs. Q4 HR: 1.57, 95% CI: 1.36-1.80). CONCLUSIONS: Our study provided novel evidence that HGS could be an independent predictor of morbidity and all-cause mortality of CM.

A review of study designs and data analyses in metabolomics studies in myopia.

Metabolomics analyzes the entire range of small molecule metabolites in biological systems to reveal the response signals that are transmitted from "genetics and environment", which could help us understand complex phenotypes of diseases. Metabolomics has been successfully applied to the study of eye diseases including age-related macular degeneration, glaucoma, and diabetic retinopathy. In this review, we summarize the findings of myopic metabolomics and discuss them from a design and analysis perspective. Finally, we provide new ideas for the future development of myopia metabolomics research based on the broader ocular metabolomics study.

Bidirectional association between depression and multimorbidity in middle-aged and elderly Chinese adults: a longitudinal cohort study.

BACKGROUND: Few studies have investigated the bidirectional association between depression and multimorbidity from a longitudinal perspective. We aimed to explore the bidirectional relationship between depression and multimorbidity in a middle-aged and elderly Chinese population. METHODS: Participants aged 45 years and older from the China Health and Retirement Longitudinal Study (CHARLS) were included. Depression was measured with a 10-item version of the Center for Epidemiological Studies Depression Scale (CESD-10). In stage I, we assessed the association of baseline depression with follow-up multimorbidity. In stage II, we examined whether multimorbidity increases the risk of depression. Logistic regression models were used to estimate the odds ratios (ORs) and confidence intervals (CIs). The ORs were then converted to risk ratios (RRs) using a proposed formula. RESULTS: A total of 7056 subjects without multimorbidity and 7587 subjects without depression at baseline were included in stage I and stage II. In stage I, the adjusted RRs (95% CIs) of depressed participants developing one disease, two diseases, three diseases, and ≥4 diseases were 1.15 (0.96-1.35), 1.64 (1.36-1.99), 1.84 (1.44-2.35) and 2.42 (1.75-3.34), respectively. In stage II, compared with individuals without any disease, the adjusted RRs (95% CIs) of developing depression for individuals carrying one disease, two diseases, three diseases, and ≥4 diseases were 1.08 (0.96-1.22), 1.39 (1.22-1.57), 1.46 (1.23-1.70) and 1.62 (1.34-1.93), respectively. CONCLUSIONS: Baseline depression increases the risk of future multimorbidity, and multimorbidity also contributes to an increased risk of incident depression in middle-aged and elderly Chinese adults.

Identification of biomarkers for essential hypertension based on metabolomics.

AIM: Essential hypertension (EH) is one of the most important public health problems worldwide. However, the pathogenesis of EH is unclear and early diagnostic methods are lacking. Metabolomics demonstrates great potential for biomarker discovery and the mechanistic exploration of metabolic diseases. DATA SYNTHESIS: This review included human and animal metabolomics studies related to EH in the PubMed and Web of Science databases between February 1996 and May 2020. The study designs, EH standards, and reported metabolic biomarkers were systematically examined and compared. The pathway analysis was conducted through the online software MetaboAnalyst 4.0. Twenty-two human studies and fifteen animal studies were included in this systematic review. There were many frequently reported biomarkers with consistent trends (e.g., pyruvate, lactic acid, valine, and tryptophan) in human and animal studies, and thus had potential as biomarkers of EH. In addition, several shared metabolic pathways, including alanine, aspartate, and glutamate metabolism, aminoacyl-tRNA biosynthesis, and arginine biosynthesis, were identified in human and animal metabolomics studies. These biomarkers and pathways, closely related to insulin resistance, the inflammatory state, and impaired nitric oxide production, were demonstrated to contribute to EH development. CONCLUSIONS: This study summarized valuable metabolic biomarkers and pathways that could offer opportunities for the early diagnosis or prediction of EH and the discovery of the metabolic mechanisms of EH.

Waist-to-height ratio, waist circumference, body mass index, waist divided by height0.5 and the risk of cardiometabolic multimorbidity: A national longitudinal cohort study.

BACKGROUND AND AIMS: Cardiometabolic multimorbidity (CM) is an increasing public health burden. This study aimed to evaluate the association of waist-to-height ratio (WHtR), waist circumference (WC), waist divided by height0.5 (WHT.5R) and body mass index (BMI) with the risk of CM. METHODS AND RESULTS: We used data from the China Health and Retirement Longitudinal Study (CHARLS). A total of 10,521 participants aged 45 years and over were recruited, including 8807 individuals with 0 cardiometabolic diseases at baseline (stage I) and 1714 individuals with 1 cardiometabolic disease at baseline (stage II). CM was defined as self-reporting of two or more of the following conditions: stroke, diabetes and heart disease. Logistic regression was conducted to estimate the odds ratios (ORs) and 95% confidence intervals (CIs). The net reclassification index (NRI) and integrated discrimination improvement (IDI) were used to evaluate the incremental predictive value beyond conventional factors. In stage I, an increased risk of CM was observed among participants with WHtR ≥0.5 (OR: 1.76, 95% CI: 1.05-2.97), WC ≥ 90 cm (men) + WC ≥ 80 cm (women) (OR: 2.06, 95% CI: 1.29-3.27), WHT.5R ≥ 6.54 cm0.5 (OR: 1.81, 95% CI: 1.16-2.83) or BMI ≥24 kg/m2 (OR: 1.48, 95% CI: 0.98-2.24). Furthermore, the NRI and IDI of WHtR, WC and WHT.5R were all higher than those of BMI. In stage II, the adjusted ORs (95% CIs) of WHtR, WC, WHT.5R and BMI were 2.04 (1.24-3.35), 1.89 (1.29-2.77), 1.86 (1.24-2.78) and 1.47 (1.06-2.04), respectively. In addition, WC exhibited the highest NRI and IDI. CONCLUSION: WHtR, WC, WHT.5R and BMI are independent predictors of CM in the middle-aged and older Chinese population. WHtR, WC and WHT.5R show better abilities in predicting CM than BMI.

Longitudinal Follow-Up Studies on the Bidirectional Association between ADL/IADL Disability and Multimorbidity: Results from Two National Sample Cohorts of Middle-Aged and Elderly Adults.

BACKGROUND AND OBJECTIVES: Few studies have investigated the bidirectional relationship between disability and multimorbidity, which are common conditions among the older population. Based on the data from the China Health and Retirement Longitudinal Study (CHARLS) and the Survey of Health, Ageing and Retirement in Europe (SHARE), we aimed to investigate the bidirectional relationship between disability and multimorbidity. METHODS: The activities of daily living (ADLs) and the instrumental activities of daily living (IADLs) scales were used to measure disability. In stage I, we used multinomial logistic regression to assess the longitudinal association between ADL/IADL disability and follow-up multimorbidity. In stage II, binary logistic regression was used to evaluate the multimorbidity effect on future disability. RESULTS: Compared with those free of disability, people with disability possessed ascending risks for developing an increasing number of diseases. For ADL disability, the odds ratio (OR) (95% confidence interval [CI]) values of developing ≥4 diseases were 4.10 (2.58, 6.51) and 6.59 (4.54, 9.56) in CHARLS and SHARE; for IADL disability, the OR (95% CI) values were 2.55 (1.69, 3.84) and 4.85 (3.51, 6.70) in CHARLS and SHARE. Meanwhile, the number of diseases at baseline was associated, in a dose-response manner, with future disability. Compared with those without chronic diseases, participants carrying ≥4 diseases had OR (95% CI) values of 4.82 (3.73, 6.21)/4.66 (3.65, 5.95) in CHARLS and 3.19 (2.59, 3.94)/3.28 (2.71, 3.98) in SHARE for developing ADL/IADL disability. CONCLUSION: The consistent findings across 2 national longitudinal studies supported a strong bidirectional association between disability and multimorbidity among middle-aged and elderly adults. Thus, tailored interventions should be taken to prevent the mutual development of disability and multimorbidity.

Role of handgrip strength in predicting new-onset diabetes: findings from the survey of health, ageing and retirement in Europe.

BACKGROUND: Diabetes is a major concern for the global health burden. This study aimed to investigate the relationship between handgrip strength (HGS) and the risk of new-onset diabetes and to compare the predictive abilities between relative HGS and dominant HGS. METHODS: This longitudinal study used data from the Survey of Health, Ageing and Retirement in Europe (SHARE), including 66,100 European participants aged 50 years or older free of diabetes at baseline. The Cox proportional hazard model was used to analyze the relationship between HGS and diabetes, and the Harrell's C index, net reclassification index (NRI), and integrated discrimination improvement (IDI) were calculated to evaluate the predictive abilities of different HGS expressions. RESULTS: There were 5,661 diabetes events occurred during follow-up. Compared with individuals with lowest quartiles, the hazard ratios (95 % confidence intervals) of the 2nd-4th quartiles were 0.88 (0.81-0.94), 0.82 (0.76-0.89) and 0.85 (0.78-0.93) for dominant HGS, and 0.95 (0.88-1.02), 0.82 (0.76-0.89) and 0.60 (0.54-0.67) for relative HGS. After adding dominant HGS to an office-based risk score (including age, gender, body mass index, smoking, and hypertension), the incremental values of the Harrell's C index, NRI, IDI of relative HGS were all slightly higher than those of dominant HGS in both training and validation sets. CONCLUSIONS: Our findings supported that HGS was an independent predictor of new-onset diabetes in the middle-aged and older European population. Moreover, relative HGS exhibited a slightly higher predictive ability than dominant HGS.

Estimation of losses of quality-adjusted life expectancy attributed to the combination of cognitive impairment and multimorbidity among Chinese adults aged 45 years and older.

OBJECTIVES: This study aims to estimate the losses of quality-adjusted life expectancy (QALE) due to the joint effects of cognitive impairment and multimorbidity, and to further confirm additional losses attributable to this interaction among middle-aged and elderly Chinese people. METHODS: The National Cause of Death Monitoring Data were linked with the China Health and Retirement Longitudinal Study (CHARLS). A mapping and assignment method was used to estimate health utility values, which were further used to calculate QALE. Losses of QALE were measured by comparing the differences between subgroups. All the losses of QALE were displayed at two levels: the individual and population levels. RESULTS: At age 45, the individual-level and population-level losses of QALE attributed to the combination of cognitive impairment and multimorbidity were 7.61 (95% CI: 5.68, 9.57) years and 4.30 (95% CI: 3.43, 5.20) years, respectively. The losses for cognitive impairment alone were 3.10 (95% CI: 2.29, 3.95) years and 1.71 (95% CI: 1.32, 2.13) years at the two levels. Similarly, the losses for multimorbidity alone were 3.53 (95% CI: 2.53, 4.56) years and 1.91 (95% CI: 1.24, 2.63) years at the two levels. Additional losses due to the interaction of cognitive impairment and multimorbidity were indicated by the 0.98 years of the individual-level gap and 0.67 years of the population-level gap. CONCLUSION: Among middle-aged and elderly Chinese people, cognitive impairment and multimorbidity resulted in substantial losses of QALE, and additional QALE losses were seen due to their interaction at both individual and population levels.

Longitudinal research on the bidirectional association between depression and arthritis.

PURPOSE: Studies evaluating the mutual relation between depression and arthritis have been limited and yielded inconsistent results. The aim of this study was to investigate the bidirectional relationship between depression and arthritis in a middle-aged and elderly Chinese population. METHODS: Participants ≥ 45 years of age were included from the China Health and Retirement Longitudinal Study (CHARLS). In stage I, we assessed the association of baseline depression with follow-up arthritis. In stage II, we examined whether the onset of arthritis predicted future depression. Logistic regression analyses were conducted to estimate the odds ratios (ORs) and confidence intervals (CIs) in stage I and stage II, respectively. RESULTS: In stage I, 24.3% (679/2794) of the depression group and 15.4% (1000/6482) of the non-depression group developed new arthritis cases. Compared with non-depression individuals, the risk of developing arthritis in depression patients was significantly higher (OR: 1.56, 95% CI 1.37-1.79). In stage II, 39.7% (973/2453) subjects in the arthritis group and 26.7% (1667/6236) subjects in the non-arthritis group developed depressive symptoms. The adjusted OR (95% CI) for depression in the arthritis group was 1.64 (1.45-1.86) times higher than that in the non-arthritis group. In the subgroup analyses according to sex, age, household income, residence, body mass index, smoking and drinking, all sub-groups yielded consistent associations. CONCLUSION: The onset of depression increased the risk of incident arthritis; in addition, baseline arthritis predicted future depression in middle-aged and elderly Chinese adults.

Differential metabolic markers associated with primary open-angle glaucoma and cataract in human aqueous humor.

BACKGROUND: We aimed to identify metabolic biomarkers and investigate the metabolic alterations in relation to primary open-angle glaucoma (POAG) and cataract in human aqueous humor. METHODS: Sixteen POAG patients undergoing surgical treatments and 24 patients undergoing cataract surgeries were included in this case-control study. We performed the metabolomic analysis of aqueous humor samples using a non-targeted gas chromatography coupled to time-of-flight mass spectrometer. The area under the receiver operating characteristic curve (AUC) was computed to assess the discrimination capacities of each metabolite marker. Databases including the Kyoto Encyclopedia of Genes and Genomes (KEGG) and MetaboAnalyst were utilized to search for the potential pathways of metabolites. RESULTS: Aqueous humor metabolomic profiles could well distinguish POAG from controls. Fourteen metabolic biomarkers were identified as potential aqueous humor biomarkers for POAG, yielding AUC values from 0.62 to 0.86. In pathway analysis, Biotin metabolism was highly impacted, implying that these metabolic markers play important roles in the regulation of this pathway. CONCLUSIONS: This study identified valuable metabolic biomarkers and pathways that may facilitate an improved understanding of the POAG pathogenesis. The finding holds translational value in the development of new therapeutic measures for POAG.

Metabolomics facilitates the discovery of metabolic biomarkers and pathways for ischemic stroke: a systematic review.

INTRODUCTION: Ischemic stroke (IS) is a major contributor to the global disease burden, and effective biomarkers for IS management in clinical practice are urgently needed. Metabolomics can detect metabolites that are small enough to cross the blood-brain barrier in a high-throughput manner, and thus represents a powerful tool for discovering biomarkers of IS. OBJECTIVES: In this study, we conducted a systematic review to identify potential metabolic biomarkers and pathways that might facilitate risk predictions, clinical diagnoses, the recognition of complications, predictions of recurrence and an understanding of the pathogenesis of IS. METHODS: The PubMed and Web of Science databases were searched for relevant studies published between January 2000 and July 2019. The study objectives, study designs and reported metabolic biomarkers were systematically examined and compared. Pathway analysis was performed using the MetaboAnalyst online software. RESULTS: Twenty-eight studies were included in this systematic review. Many consistent metabolites, including isoleucine, leucine, valine, glycine, lysine, glutamate, LysoPC(16:0), LysoPC(18:2), serine, uric acid, citrate and palmitic acid, possess potential as biomarkers of IS. Metabolic pathways and dysregulations that are implicated in excitotoxicity, inflammation, apoptosis, oxidative stress, neuroprotection, energy failure, and elevation of intracellular Ca2+ levels, were indicated as playing important roles in the development and progression of IS. CONCLUSIONS: This systematic review summarizes potential metabolic biomarkers and pathways related to IS, which may provide opportunities for the construction of diagnostic or predictive models for IS and the discovery of novel therapeutic targets.

Exposure to Chinese famine in early life modifies the association between hyperglycaemia and cardiovascular disease.

BACKGROUND AND AIMS: The Great Leap Forward Famine during 1959-1961 was the world's largest famine, and its adverse long-term effects might be more apparent in the coming decade with ageing of the exposed populations. The aim of this study was to examine whether the Chinese Famine modified the effect of hyperglycaemia on cardiovascular disease (CVD). METHODS AND RESULTS: We used data of 4337 adults born between 1952 and 1964 collected from the China Health and Retirement Longitudinal Study (CHARLS). Logistic regression was used to estimate the odds ratios (ORs) and confidence intervals (CIs) between hyperglycaemia and CVD. The prevalence of CVD showed significant difference among different famine exposure cohorts (P = 0.0156). After multivariable adjustment, the ORs (95% CIs) were as follows: 1.46 (0.94, 2.26) for late childhood, 1.76 (1.06, 2.90) for mid childhood, 1.40 (0.86, 2.27) for early childhood, 2.55 (1.30, 5.02) for the foetal cohort and 1.10 (0.63, 1.95) for the non-exposed cohort. There was a significant interaction between hyperglycaemia and famine exposure for CVD (P = 0.0374). In addition, the subgroup analyses showed that the effect of hyperglycaemia on CVD in the foetal exposure cohort was significantly higher than those in any of the other famine-exposed cohorts, especially in those who lived in rural areas (OR: 4.67, 95% CI: 1.70-12.84), those who lived in severe famine areas (OR: 5.01, 95% CI: 1.22-20.66) and those who were men (OR: 3.66, 95% CI: 1.01-13.33). CONCLUSION: Exposure to the Chinese Famine, especially during the foetal stage of life, aggravated the association between hyperglycaemia and CVD.

Metabolomic characterization of hypertension and dyslipidemia.

BACKGROUND: Hypertension and dyslipidemia are two main risk factors for cardiovascular diseases (CVD). Moreover, their coexistence predisposes individuals to a considerably increased risk of CVD. However, the regulatory mechanisms involved in hypertension and dyslipidemia as well as their interactions are incompletely understood. OBJECTIVES: The aims of our study were to identify metabolic biomarkers and pathways for hypertension and dyslipidemia, and compare the metabolic patterns between hypertension and dyslipidemia. METHODS: In this study, we performed metabolomic investigations into hypertension and dyslipidemia based on a "healthy" UK population. Metabolomic data from the Husermet project were acquired by gas chromatography-mass spectrometry and ultra-performance liquid chromatography-mass spectrometry. Both univariate and multivariate statistical methods were used to facilitate biomarker selection and pathway analysis. RESULTS: Serum metabolic signatures between individuals with and without hypertension or dyslipidemia exhibited considerable differences. Using rigorous selection criteria, 26 and 46 metabolites were identified as potential biomarkers of hypertension and dyslipidemia respectively. These metabolites, mainly involved in fatty acid metabolism, glycerophospholipid metabolism, alanine, aspartate and glutamate metabolism, are implicated in insulin resistance, vascular remodeling, macrophage activation and oxidised LDL formation. Remarkably, hypertension and dyslipidemia exhibit both common and distinct metabolic patterns, revealing their independent and synergetic biological implications. CONCLUSION: This study identified valuable biomarkers and pathways for hypertension and dyslipidemia, and revealed common and different metabolic patterns between hypertension and dyslipidemia. The information provided in this study could shed new light on the pathologic mechanisms and offer potential intervention targets for hypertension and dyslipidemia as well as their related diseases.

Predicting ovarian cancer recurrence by plasma metabolic profiles before and after surgery.

BACKGROUND: Previous metabolomic studies have revealed that plasma metabolic signatures may predict epithelial ovarian cancer (EOC) recurrence. However, few studies have performed metabolic profiling of pre- and post-operative specimens to investigate EOC prognostic biomarkers. OBJECTIVE: The aims of our study were to compare the predictive performance of pre- and post-operative specimens and to create a better model for recurrence by combining biomarkers from both metabolic signatures. METHODS: Thirty-five paired plasma samples were collected from 35 EOC patients before and after surgery. The patients were followed-up until December, 2016 to obtain recurrence information. Metabolomics using rapid resolution liquid chromatography-mass spectrometry was performed to identify metabolic signatures related to EOC recurrence. The support vector machine model was employed to predict EOC recurrence using identified biomarkers. RESULTS: Global metabolomic profiles distinguished recurrent from non-recurrent EOC using both pre- and post-operative plasma. Ten common significant biomarkers, hydroxyphenyllactic acid, uric acid, creatinine, lysine, 3-(3,5-diiodo-4-hydroxyphenyl) lactate, phosphohydroxypyruvic acid, carnitine, coproporphyrinogen, L-beta-aspartyl-L-glutamic acid and 24,25-hydroxyvitamin D3, were identified as predictive biomarkers for EOC recurrence. The area under the receiver operating characteristic (AUC) values in pre- and post-operative plasma were 0.815 and 0.909, respectively; the AUC value after combining the two sets reached 0.964. CONCLUSION: Plasma metabolomic analysis could be used to predict EOC recurrence. While post-operative biomarkers have a predictive advantage over pre-operative biomarkers, combining pre- and post-operative biomarkers showed the best predictive performance and has great potential for predicting recurrent EOC.

Large-scale profiling of metabolic dysregulation in ovarian cancer.

Ovarian cancer is the leading cause of death in gynecologic malignancies. Profiling of endogenous metabolites has potential to identify changes caused by cancer and provide inspiring insights into cancer metabolism. To systematically investigate ovarian cancer metabolism, we performed metabolic profiling of 448 plasma samples related to epithelial ovarian cancer (EOC) based on ultra-performance liquid chromatography mass spectrometry in both positive and negative modes. These unbiased metabolomic profiles could well distinguish EOC from benign ovarian tumor (BOT) and uterine fibroid (UF). Fifty-three metabolites were identified as specific biomarkers for EOC, and this is the first report of piperine, 3-indolepropionic acid, 5-hydroxyindoleacetaldehyde and hydroxyphenyllactate as metabolic biomarkers of EOC. The AUC values of these metabolites for discriminating EOC from BOT/UF and early-stage EOC from BOT/UF were 0.9100/0.9428 and 0.8385/0.8624, respectively. Meanwhile, our metabolites were able to distinguish early-stage EOC from late-stage EOC with an AUC of 0.8801. Importantly, analysis of dysregulated metabolic pathways extends our current understanding of EOC metabolism. Metabolic pathways in EOC patients are mainly characterized by abnormal phospholipid metabolism, altered l-tryptophan catabolism, aggressive fatty acid ß-oxidation and aberrant metabolism of piperidine derivatives. Together, these metabolic pathways provide a foundation to support cancer development and progression. In conclusion, our large-scale plasma metabolomics study yielded fundamental insights into dysregulated metabolism in ovarian cancer, which could facilitate clinical diagnosis, therapy, prognosis and shed new lights on ovarian cancer pathogenesis.

Noninvasive detection of colorectal cancer by analysis of exhaled breath.

There has been growing interest in exhaled breath analysis for cancer screening and disease monitoring; however, limited breath biomarker information exists regarding colorectal cancer (CRC). The objective of this study was to screen for breath biomarkers of CRC. Exhaled breath was collected from 20 CRC patients and 20 healthy controls; subsequently, solid-phase microextraction-gas chromatography/mass spectrometry (SPME-GC/MS) was used to assess the exhaled volatile organic compounds (VOCs) of the study participants. The statistical methods of principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were performed to process the final data. The VOCs in the exhalations of CRC patients exhibited significant differences from the VOCs in the exhalations of healthy controls; in particular, relative to the latter exhalations, the former exhalations contain significantly higher levels of cyclohexanone, 2,2-dimethyldecane, dodecane, 4-ethyl-1-octyn-3-ol, ethylaniline, cyclooctylmethanol, trans-2-dodecen-1-ol, and 3-hydroxy-2,4,4-trimethylpentyl 2-methylpropanoate but significantly lower levels of 6-t-butyl-2,2,9,9-tetramethyl-3,5-decadien-7-yne (P < 0.05). Analyses of breath VOCs provide a related model of CRC exhalation that could represent an effective and convenient screening method for this disease.