Quantifying depressive symptoms on incidence of common chronic diseases and multimorbidity patterns in middle-aged and elderly Chinese adults.

BACKGROUND: Depressive symptoms are highly prevalent and increase risks of various morbidities. However, the extent to which depressive symptoms could account for incidence of these chronic conditions, in particular multimorbidity patterns, remains to be examined and quantified. METHODS: For this cohort analysis, we included 9024-14,093 participants aged 45 years and older from the China Health and Retirement Longitudinal Study (CHARLS). Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for the longitudinal associations between depressive symptoms and 13 common chronic diseases and 4 multimorbidity patterns. Population attributable fractions (PAFs) combining the information on both exposure prevalence and risk association were estimated to quantify the magnitude of the burden of these conditions attributable to depressive symptoms. RESULTS: Depressive symptoms were associated with increased risks of liver disease, stroke, heart problem, asthma, diabetes, arthritis, kidney disease, chronic lung disease, digestive disease, dyslipidemia, and memory-related disease, and the adjusted HRs (95% CIs) and PAFs (95% CIs) ranged from 1.15 (1.05-1.26) to 1.64 (1.38-1.96) and 5% (0-10%) to 17% (6-28%), respectively. In addition, individuals with depressive symptoms had elevated risks of the cardiometabolic-cancer pattern, the cerebrovascular-memory pattern, the articular-visceral organ pattern, and the respiratory pattern, with respective HRs (95% CIs) of 1.26 (1.11-1.42), 1.34 (1.07-1.69), 1.45 (1.29-1.63), and 2.01 (1.36-2.96), and respective PAFs (95% CIs) of 5% (0-10%), 8% (-4-21%), 12% (7-17%), and 20% (5-35%). CONCLUSION: Depressive symptoms contribute substantially to the burden across a broad range of chronic diseases as well as different multimorbidity patterns in middle-aged and older Chinese.

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.

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.

Application of metabolomics in the diagnosis of breast cancer: a systematic review.

Breast cancer (BC) remains the most frequent type of cancer in females worldwide. However, the pathogenesis of BC is still under the cloud, along with the huge challenge of early diagnosis, which is widely acknowledged as the key to a successful therapy. Metabolomics, a newborn innovative technique in recent years, has demonstrated great potential in cancer-related researches. The aim of this review is to look back on clinical and cellular metabolomic studies in the diagnosis of BC over the past decade, and provide a systematic summary of metabolic biomarkers and pathways related to BC diagnosis.

High-sensitivity C-reactive protein and cystatin C independently and jointly predict all-cause mortality among the middle-aged and elderly Chinese population.

Studies investigating the relationship between high-sensitivity C-reactive protein (hs-CRP), cystatin C, and all-cause mortality yielded inconsistent results. Moreover, the joint effect of hs-CRP and cystatin C on mortality risk is largely unknown for the general population. In this study, we examined the associations between hs-CRP, cystatin C, and all-cause mortality using data from the China Health and Retirement Longitudinal Study (CHARLS). Middle-aged and elderly participants with complete data were enrolled for a 4-year follow-up of total mortality and plasma levels of hs-CRP (n = 11,409) and cystatin C (n = 8680). In study population, the highest quartiles of hs-CRP and cystatin C were significantly associated with increased total mortality risk compared with the lowest quartile, and adjusted hazard ratios (95% confidence intervals) were 2.08 (1.49-2.91) and 1.97 (1.33-2.94) for hs-CRP and cystatin C, respectively. Remarkably, the adjusted hazard ratio (95% confidence interval) of the co-occurrence of elevated hs-CRP and increased cystatin C was 4.17 (2.94-5.92), in contrast to each elevation alone: 1.89 (1.45-2.47) for hs-CRP and 2.08 (1.46-2.97) for cystatin C. Moreover, a subgroup analysis by gender yielded similar associations. Lastly, the addition of hs-CRP and cystatin C to conventional factors significantly improved risk prediction of total mortality (net reclassification index 0.3622, P < 0.0001; integrated discrimination improvement 0.0354, P < 0.0001). Taken together, findings suggest that plasma hs-CRP and cystatin C serve as independent predictors of all-cause mortality among the middle-aged and elderly Chinese population. Furthermore, the combination of hs-CRP and cystatin C could predict overall mortality better than each component individually.

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.

Use of Plasma Metabolomics to Identify Diagnostic Biomarkers for Early Stage Epithelial Ovarian Cancer.

The early detection of ovarian carcinoma is difficult due to the absence of recognizable physical symptoms and a lack of sensitive screening methods. The currently available biomarkers (such as CA125 and HE4) are insufficiently reliable to distinguish early stage (I/II) epithelial ovarian cancer (EOC) patients from normal individuals because they possess a relatively poor sensitivity and specificity. To evaluate the application of metabolomics to biomarker discovery in the early stages of epithelial ovarian cancer (EOC), plasma samples from 21 early stage EOC patients and 31 healthy controls were analyzed with ultra-performance liquid chromatography quadrupole time-of-flight mass spectrometry (UPLC/Q-Tof/MS) in conjunction with multivariate statistical analysis. Eighteen metabolites, including lysophospholipids, 2-piperidone and MG (18:2), were found to be disturbed in early stage EOC with satisfactory diagnostic accuracy (AUC=0.920). These biomarkers were specifically validated in the EOC nude mouse model, and five of the biomarkers (lysophospholipids, adrenoyl ethanolamide et al.) were highly suspected of being associated with EOC because they were differentially expressed with the same tendency in the EOC nude mice versus normal controls. In conclusion, the selected metabolic biomarkers have considerable utility and significant potential for diagnosing early ovarian cancer and investigating its underlying mechanisms.

Metabolic phenotyping for monitoring ovarian cancer patients.

Epithelial ovarian cancer (EOC) is the most deadly of the gynecological cancers. New approaches and better tools for monitoring treatment efficacy and disease progression of EOC are required. In this study, metabolomics using rapid resolution liquid chromatography mass spectrometry was applied to a systematic investigation of metabolic changes in response to advanced EOC, surgery and recurrence. The results revealed considerable metabolic differences between groups. Moreover, 37, 30, and 26 metabolites were identified as potential biomarkers for primary, surgical and recurrent EOC, respectively. Primary EOC was characterized by abnormal lipid metabolism and energy disorders. Oxidative stress and surgical efficacy were clear in the post-operative EOC patients. Recurrent EOC patients showed increased amino acid and lipid metabolism compared with primary EOC patients. After cytoreductive surgery, eight metabolites (e.g. l-kynurenine, retinol, hydroxyphenyllactic acid, 2-octenoic acid) corrected towards levels of the control group, and four (e.g. hydroxyphenyllactic acid, 2-octenoic acid) went back again to primary EOC levels after disease relapse. In conclusion, this study delineated metabolic changes in response to advanced EOC, surgery and recurrence, and identified biomarkers that could facilitate both understanding and monitoring of EOC development and progression.

Exhaled breath volatile biomarker analysis for thyroid cancer.

Compared with other types of cancer, thyroid cancer incidence rates have increased rapidly worldwide in the past few decades. In recent years, potential thyroid cancer biomarkers have been studied, but these biomarkers have neither specificity nor good positive predictive value. Exhaled breath analysis is a recently developed convenient and noninvasive method for screening and diagnosing the disease. In this study, potential thyroid cancer biomarkers in volatile organic compounds (VOCs) were detected. Exhaled breath was collected from 64 patients with histologically confirmed cases of thyroid disease (including 39 individuals with papillary thyroid carcinoma and 25 individuals with nodular goiters) and 32 healthy volunteers. Solid-phase microextraction-gas chromatography and mass spectrometry was used to assess the exhaled VOCs of the study participants. The statistical methods of principal component analysis and partial least-squares discriminant analysis were performed to process the final data. The VOCs exhibited significant differences between nodular goiter patients and normal controls, papillary thyroid carcinoma patients and normal controls, and papillary thyroid carcinoma patients and nodular goiter patients; 7, 7, and 3 characteristic metabolites played decisive roles in sample classification, respectively. Breath analysis may provide a new, noninvasive, and directly qualitative method for the clinical diagnosis of thyroid disease.

Prediction of advanced ovarian cancer recurrence by plasma metabolic profiling.

Epithelial ovarian cancer (EOC) is the most lethal of gynecologic malignancies due to the high rate of recurrence and poor prognosis. Predicting the prognosis in patients with EOC is clinically challenging, partly because appropriate biomarkers of recurrence have yet to be explored. In this prospective study, pre-treatment plasma samples were collected from 38 patients with stage III or IV EOC who were subsequently followed up. Ultra-performance liquid chromatography mass spectrometry was used to perform metabolic profiling, which yielded five metabolites that were potential biomarkers for EOC recurrence: l-tryptophan, kynurenine, bilirubin, LysoPC (14 : 0) and LysoPE (18 : 2). A combination of these five potential biomarkers strongly predicted recurrence, the area under the curve being 0.91. In summary, the candidate biomarkers identified in this study may both facilitate clinical prediction of EOC recurrence and prognosis and serve as potential therapeutic targets in patients with 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.

Exhaled volatile organic compounds as lung cancer biomarkers during one-lung ventilation.

In this study, single-lung ventilation was used to detect differences in the volatile organic compound (VOCs) profiles between lung tissues in healthy and affected lungs. In addition, changes that occurred after lung cancer resection in both the VOCs profiles of exhaled breath from ipsilateral and contralateral lungs and the VOCs profiles of exhaled breath and blood sample headspaces were also determined. Eighteen patients with non-small cell carcinoma were enrolled. Alveolar breath samples were taken separately from healthy and diseased lungs before and after the tumor resection. Solid phase microextraction-gas chromatography/mass spectrometry was used to assess the exhaled VOCs of the study participants. The VOCs exhibited significant differences between the contralateral and ipsilateral lungs before surgery, the contralateral and ipsilateral lungs after surgery, the ipsilateral lungs before and after surgery, and the blood samples from before and after surgery; 12, 19, 12 and 5 characteristic metabolites played decisive roles in sample classification, respectively. 2,2-Dimethyldecane, tetradecane, 2,2,4,6,6-pentamethylheptane, 2,3,4-trimethyldecane, nonane, 3,4,5,6-tetramethyloctane, and hexadecane may be generated from lipid peroxidation during surgery. Caprolactam and propanoic acid may be more promising exhaled breath biomarkers for lung cancer.

Volatile organic metabolites identify patients with breast cancer, cyclomastopathy, and mammary gland fibroma.

The association between cancer and volatile organic metabolites in exhaled breaths has attracted increasing attention from researchers. The present study reports on a systematic study of gas profiles of metabolites in human exhaled breath by pattern recognition methods. Exhaled breath was collected from 85 patients with histologically confirmed breast disease (including 39 individuals with infiltrating ductal carcinoma, 25 individuals with cyclomastopathy and from 21 individuals with mammary gland fibroma) and 45 healthy volunteers. Principal component analysis and partial least squares discriminant analysis were used to process the final data. The volatile organic metabolites exhibited significant differences between breast cancer and normal controls, breast cancer and cyclomastopathy, and breast cancer and mammary gland fibroma; 21, 6, and 8 characteristic metabolites played decisive roles in sample classification, respectively (P < 0.05). Three volatile organic metabolites in the exhaled air, 2,5,6-trimethyloctane, 1,4-dimethoxy-2,3-butanediol, and cyclohexanone, distinguished breast cancer patients from healthy individuals, mammary gland fibroma patients, and patients with cyclomastopathy (P < 0.05). The identified three volatile organic metabolites associated with breast cancer may serve as novel diagnostic biomarkers.

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.

Blood volatile compounds as biomarkers for colorectal cancer.

Many recent studies have focused on the connection between the composition of specific volatile organic compounds (VOCs) in exhaled breath and various forms of cancer. However, the composition of exhaled breath is affected by many factors, such as lung disease, smoking, and diet. VOCs are released into the bloodstream before they are exhaled; therefore, the analysis of VOCs in blood will provide more accurate results than the analysis of VOCs in exhaled breath. Blood were collected from 16 colorectal cancer patients and 20 healthy controls, then solid phase microextraction-chromatography-mass spectrometry (SPME-GC-MS) was used to analysis the exhaled volatile organic compounds (VOCs). The statistical methods principal component analysis (PCA) and partial least-squares discriminant analysis (PLSDA) were performed to deal with the final dates. Three metabolic biomarkers were found at significantly lower levels in the group of CRC patients than in the normal control group (P<0.01): phenyl methylcarbamate, ethylhexanol, and 6-t-butyl-2,2,9,9-tetramethyl-3,5-decadien-7-yne. In addition, significantly higher levels of 1,1,4,4-tetramethyl-2,5-dimethylene-cyclohexane were found in the group of CRC patients than in the normal control group (P<0.05). Compared with healthy individuals, patients with colorectal adenocarcinoma exhibited a distinct blood metabolic profile with respect to VOCs. The analysis of blood VOCs appears to have potential clinical applications for CRC screening.

Identification of potential biomarkers for ovarian cancer by urinary metabolomic profiling.

To evaluate the application of urinary metabolomics on discovering potential biomarkers for epithelial ovarian cancer (EOC), urine samples from 40 preoperative EOC patients, 62 benign ovarian tumor (BOT) patients, and 54 healthy controls were collected and analyzed with ultraperformance liquid chromatography quadrupole time-of-flight mass spectrometry (UPLC-QTOF/MS). Good separations were obtained for EOC vs BOT, EOC vs healthy controls analyzed by partial least-squares discriminant analysis, or principal component analysis. Twenty-two ascertained metabolomic biomarkers were found to be disturbed in several metabolic pathways among EOC patients, including nucleotide metabolism (pseudouridine, N4-acetylcytidine), histidine metabolism (L-histidine, imidazol-5-yl-pyruvate), tryptophan metabolism (3-indolelactic acid), and mucin metabolism (3'-sialyllactose and 3-sialyl-N-acetyllactosamine). In addition, the concentrations of some urinary metabolites of 18 postoperative EOC patients among the 40 EOC patients changed significantly compared with those of their preoperative condition, and four of them suggested recovery tendency toward normal level after surgical operation, including N4-acetylcytidine, pseudouridine, urate-3-ribonucleoside, and succinic acid. These metabolites would be highly postulated to be associated with EOC. In conclusion, our study demonstrated that urinary metabolomics analysis by UPLC-QTOF/MS, performed in a minimally noninvasive and convenient manner, possessed great potential in biomarker discovery for EOC.

Discrimination between malignant and benign ovarian tumors by plasma metabolomic profiling using ultra performance liquid chromatography/mass spectrometry.

BACKGROUND: Discrimination between epithelial ovarian cancer (EOC) and benign ovarian tumor (BOT) has always been difficult in clinical practice. We investigated the application of metabolomics in distinguishing EOC and BOT and tried to discover valuable biomarkers. METHODS: Plasma metabolomic profiling was performed using ultra-performance liquid chromatography mass spectrometry (UPLC/MS). Partial least-squares discriminant analysis was employed to classify EOC and BOT, and reveal their metabolic differences. The area under the receiver-operating characteristic curve (AUC) was utilized to evaluate the predictive performance of the metabolic profiles for external validation set. RESULTS: The metabolomic profiles consisting of 535 metabolites revealed a clear separation between EOC and BOT, with AUC of 0.86 for the external validation set. 6 metabolic biomarkers were identified, and the plasma concentrations of the 4 ascertained biomarkers (L-tryptophan, LysoPC(18:3), LysoPC(14:0), and 2-Piperidinone) were lower in EOC patients than those in BOT patients. Among them, tryptophan and LysoPC have been suspected to participate in cancer progression, and 2-Piperidinone might be a novel biomarker for EOC. CONCLUSIONS: Metabolomics could be used to discriminate EOC from BOT in clinical practice, and the identified metabolic biomarkers might be important on investigating the biological mechanisms of EOC.