Adherence to the EAT-lancet diet and incident depression and anxiety.

High-quality diets have been increasingly acknowledged as a promising candidate to counter the growing prevalence of mental health disorders. This study aims to investigate the prospective associations of adhering to the EAT-Lancet reference diet with incident depression, anxiety and their co-occurrence in 180,446 UK Biobank participants. Degrees of adherence to the EAT-Lancet diet were translated into three different diet scores. Over 11.62 years of follow-up, participants in the highest adherence group of the Knuppel EAT-Lancet index showed lower risks of depression (hazard ratio: 0.806, 95% CI: 0.730-0.890), anxiety (0.818, 0.751-0.892) and their co-occurrence (0.756, 0.624-0.914), compared to the lowest adherence group. The corresponding hazard ratios (95% CIs) were 0.711 (0.627-0.806), 0.765 (0.687-0.852) and 0.659 (0.516-0.841) for the Stubbendorff EAT-Lancet index, and 0.844 (0.768-0.928), 0.825 (0.759-0.896) and 0.818 (0.682-0.981) for the Kesse-Guyot EAT-Lancet diet index. Our findings suggest that higher adherence to the EAT-Lancet diet is associated with lower risks of incident depression, anxiety and their co-occurrence.

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.

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.

Metabolomics on depression: A comparison of clinical and animal research.

BACKGROUND: Depression is a major cause of suicide and mortality worldwide. This study aims to conduct a systematic review to identify metabolic biomarkers and pathways for major depressive disorder (MDD), a prevalent subtype of clinical depression. METHODS: We searched for metabolomics studies on depression published between January 2000 and January 2023 in the PubMed and Web of Science databases. The reported metabolic biomarkers were systematically evaluated and compared. Pathway analysis was implemented using MetaboAnalyst 5.0. RESULTS: We included 26 clinical studies on MDD and 78 metabolomics studies on depressive-like animal models. A total of 55 and 77 high-frequency metabolites were reported consistently in two-thirds of clinical and murine studies, respectively. In the comparison between murine and clinical studies, we identified 9 consistently changed metabolites (tryptophan, tyrosine, phenylalanine, methionine, fumarate, valine, deoxycholic acid, pyruvate, kynurenic acid) in the blood, 1 consistently altered metabolite (indoxyl sulfate) in the urine and 14 disturbed metabolic pathways in both types of studies. These metabolic dysregulations and pathways are mainly implicated in enhanced inflammation, impaired neuroprotection, reduced energy metabolism, increased oxidative stress damage and disturbed apoptosis, laying solid molecular foundations for MDD. LIMITATIONS: Due to unavailability of original data like effect-size results in many metabolomics studies, a meta-analysis cannot be conducted, and confounding factors cannot be fully ruled out. CONCLUSIONS: This systematic review delineated metabolic biomarkers and pathways related to depression in the murine and clinical samples, providing opportunities for early diagnosis of MDD and the development of novel diagnostic targets.

Preserved Ratio Impaired Spirometry and Risks of Macrovascular, Microvascular Complications and Mortality Among Individuals With Type 2 Diabetes.

BACKGROUND: The prospective associations of preserved ratio impaired spirometry (PRISm) with new-onset macrovascular and microvascular complications and mortality among individuals with type 2 diabetes (T2D) and whether PRISm enhances the prediction ability of an established office-based risk score remain to be elucidated. RESEARCH QUESTION: Can PRISm be used as a predictor of poor prognosis in individuals with T2D? STUDY DESIGN AND METHODS: We included 20,047 study participants with T2D and complete data on spirometry at recruitment from the UK Biobank cohort. Multivariable Cox proportional hazards models were used to assess the associations of baseline PRISm (FEV1 to FVC ratio, ≥ 0.70; FEV1, < 80% predicted) with subsequent risks of incident stroke (any type), ischemic stroke, myocardial infarction, unstable angina, coronary heart disease, diabetic retinopathy, diabetic kidney disease, all-cause mortality, cardiovascular mortality, and respiratory mortality. RESULTS: For this cohort analysis, 4,521 patients (22.55% of participants with T2D) showed comorbid PRISm at baseline. Over a median follow-up of 11.52 to 11.87 years, patients with T2D with PRISm at baseline showed higher risks than those with normal spirometry findings of various T2D complications developing and mortality; the adjusted hazard ratios for PRISm were 1.413 (95% CI, 1.187-1.681) for stroke (any type), 1.382 (95% CI, 1.129-1.690) for ischemic stroke, 1.253 (95% CI, 1.045-1.503) for myocardial infarction, 1.206 (95% CI, 1.086-1.339) for coronary heart disease, 1.311 (95% CI, 1.141-1.506) for diabetic retinopathy, 1.384 (95% CI, 1.190-1.610) for diabetic kidney disease, 1.337 (95% CI, 1.213-1.474) for all-cause mortality, 1.597 (95% CI, 1.296-1.967) for cardiovascular mortality, and 1.559 (95% CI, 1.189-2.044) for respiratory mortality, respectively. The addition of PRISm significantly improved the reclassification ability, based on the net reclassification index, of an office-based risk score by 15.53% (95% CI, 10.14%-19.63%) to 33.60% (95% CI, 20.90%-45.79%). INTERPRETATION: Individuals with T2D with comorbid PRISm, accounting for a considerable proportion of the population with T2D, showed significantly increased risks of adverse macrovascular and microvascular complications and mortality.

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.

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.

Preserved Ratio Impaired Spirometry, Metabolomics, and the Risk of Type 2 Diabetes.

CONTEXT: Whether baseline preserved ratio impaired spirometry (PRISm) is associated with the risk of developing type 2 diabetes (T2D) and if this association could be mediated by circulating metabolites remains to be elucidated. OBJECTIVE: To measure the prospective association of PRISm with T2D and potential metabolic mediators thereof. METHODS: This study used data from the UK Biobank and included 72 683 individuals without diabetes at baseline. PRISm was defined as the predicted forced expiratory volume in 1 second (FEV1) <80% and the FEV1/forced vital capacity ratio ≥0.70. Cox proportional hazards modeling was performed to assess the longitudinal relation between baseline PRISm and incident T2D. Mediation analysis was used to explore the mediation effects of circulating metabolites in the path from PRISm to T2D. RESULTS: During a median follow-up of 12.06 years, 2513 participants developed T2D. Individuals who had PRISm (N = 8394) were 47% (95% CI, 33%-63%) more likely to develop T2D compared with those who had normal spirometry (N = 64 289). A total of 121 metabolites showed statistically significant mediation effects in the path from PRISm to T2D (false discovery rate <0.05). Glycoprotein acetyls, cholesteryl esters in large high-density lipoprotein (HDL), degree of unsaturation, cholesterol in large HDL, and cholesteryl esters in very large HDL were the top 5 metabolic markers, with mediation proportions (95% CI) being 11.91% (8.76%-16.58%), 11.04% (7.34%-15.55%), 10.36% (7.34%-14.71%), 9.87% (6.78%-14.09%), and 9.51% (6.33%-14.05%), respectively. A total of 11 principal components that explained 95% variance of the metabolic signatures accounted for 25.47% (20.83%-32.19%) of the relation between PRISm and T2D. CONCLUSIONS: Our study revealed the association of PRISm with T2D risk and the potential roles of circulating metabolites in mediating this association.

Machine Learning-Based Integration of Metabolomics Characterisation Predicts Progression of Myopic Retinopathy in Children and Adolescents.

Myopic retinopathy is an important cause of irreversible vision loss and blindness. As metabolomics has recently been successfully applied in myopia research, this study sought to characterize the serum metabolic profile of myopic retinopathy in children and adolescents (4-18 years) and to develop a diagnostic model that combines clinical and metabolic features. We selected clinical and serum metabolic data from children and adolescents at different time points as the training set (n = 516) and the validation set (n = 60). All participants underwent an ophthalmologic examination. Untargeted metabolomics analysis of serum was performed. Three machine learning (ML) models were trained by combining metabolic features and conventional clinical factors that were screened for significance in discrimination. The better-performing model was validated in an independent point-in-time cohort and risk nomograms were developed. Retinopathy was present in 34.2% of participants (n = 185) in the training set, including 109 (28.61%) with mild to moderate myopia. A total of 27 metabolites showed significant variation between groups. After combining Lasso and random forest (RF), 12 modelled metabolites (mainly those involved in energy metabolism) were screened. Both the logistic regression and extreme Gradient Boosting (XGBoost) algorithms showed good discriminatory ability. In the time-validation cohort, logistic regression (AUC 0.842, 95% CI 0.724-0.96) and XGBoost (AUC 0.897, 95% CI 0.807-0.986) also showed good prediction accuracy and had well-fitted calibration curves. Three clinical characteristic coefficients remained significant in the multivariate joint model (p < 0.05), as did 8/12 metabolic characteristic coefficients. Myopic retinopathy may have abnormal energy metabolism. Machine learning models based on metabolic profiles and clinical data demonstrate good predictive performance and facilitate the development of individual interventions for myopia in children and adolescents.

Metabolomics facilitates the discovery of metabolic profiles and pathways for myopia: A systematic review.

BACKGROUND: Myopia is one of the major eye disorders and the global burden is increasing rapidly. Our purpose is to systematically summarize potential metabolic biomarkers and pathways in myopia to facilitate the understanding of disease mechanisms as well as the discovery of novel therapeutic measures. METHODS: Myopia-related metabolomics studies were searched in electronic databases of PubMed and Web of Science until June 2021. Information regarding clinical and demographic characteristics of included studies and metabolomics findings were extracted. Myopia-related metabolic pathways were analysed for differential metabolic profiles, and the quality of included studies was assessed based on the QUADOMICS tool. Pathway analyses of differential metabolites were performed using bioinformatics tools and online software such as the Metaboanalyst 5.0. RESULTS: The myopia-related metabolomics studies included in this study consisted of seven human and two animal studies. The results of the study quality assessment showed that studies were all phase I studies and all met the evaluation criteria of 70% or more. The myopia-control serum study identified 23 differential metabolites with the Sphingolipid metabolism pathway beings enriched. The high myopia-cataract aqueous humour study identified 40 differential metabolites with the Arginine biosynthesis pathway being enriched. The high myopia-control serum study identified 43 differential metabolites and 4 pathways were significantly associated with metabolites including Citrate cycle; Alanine, aspartate and glutamate metabolism; Glyoxylate and dicarboxylate metabolism; Biosynthesis of unsaturated fatty acids (all P value < 0.05). CONCLUSIONS: This study summarizes potential metabolic biomarkers and pathways in myopia, providing new clues to elucidate disease mechanisms.

Role of depression in the development of cardiometabolic multimorbidity: Findings from the UK Biobank study.

BACKGROUND: Depression has been acknowledged as a risk factor for cardiometabolic diseases, but its role in the development of cardiometabolic multimorbidity (CM) remains unclear. We aimed to prospectively investigate how depression affected the development of CM based on the UK Biobank study. METHODS: We included 459,747 participants with none or one prior cardiometabolic disease. Depression was assessed by the clinical diagnosis and Patient Health Questionnaire (PHQ-2). CM was defined as the coexistence of two or more conditions of type 2 diabetes, stroke, and coronary heart disease (CHD). Multistate models were used to examine the role of depression in the transitions from baseline to single cardiometabolic diseases and subsequently to CM. RESULTS: During a median follow-up of 12.07 years, we documented 3413 incident CM cases among initially disease-free participants and 7461 cases among participants with one prior cardiometabolic disease, respectively. The hazard of developing CM associated with depression was higher among disease-free individuals than that among individuals with one cardiometabolic disease (HR, 95 % CI: 1.68, 1.54-1.83 vs 1.28, 1.20-1.35). Moreover, depression was significantly associated with all transitions from baseline to diabetes (HR, 95 % CI: 1.43, 1.37-1.50), stroke (1.28, 1.20-1.38), and CHD (1.35, 1.31-1.40). After the onset of these cardiometabolic diseases, the HR values (95 % CIs) of progression to CM were 1.26 (1.09-1.46) for diabetes, 1.43 (1.16-1.76) for stroke, and 1.23 (1.08-1.40) for CHD. CONCLUSIONS: Depression was an independent risk factor for CM, and adversely influenced the whole progression from disease-free status to CM.

Metabolomics and Biomarkers in Retinal and Choroidal Vascular Diseases.

The retina is one of the most important structures in the eye, and the vascular health of the retina and choroid is critical to visual function. Metabolomics provides an analytical approach to endogenous small molecule metabolites in organisms, summarizes the results of "gene-environment interactions", and is an ideal analytical tool to obtain "biomarkers" related to disease information. This study discusses the metabolic changes in neovascular diseases involving the retina and discusses the progress of the study from the perspective of metabolomics design and analysis. This study advocates a comparative strategy based on existing studies, which encompasses optimization of the performance of newly identified biomarkers and the consideration of the basis of existing studies, which facilitates quality control of newly discovered biomarkers and is recommended as an additional reference strategy for new biomarker discovery. Finally, by describing the metabolic mechanisms of retinal and choroidal neovascularization, based on the results of existing studies, this study provides potential opportunities to find new therapeutic approaches.

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.

Cohort profile: the Liyang cohort study on chronic diseases and risk factors monitoring in China (Liyang Study).

PURPOSE: The Liyang cohort study on chronic diseases and risk factors monitoring in China (Liyang Study) is a prospective population-based study which aims to investigate and identify the determinants of the most prevalent chronic non-communicable diseases (NCDs) and to evaluate the impact of demographic characteristics, lifestyle, dietary habits, cognition, disability and NCDs on the health-related quality of life. PARTICIPANTS: Between March 2019 and June 2020, 10 056 individuals aged ≥18 years were administered a baseline survey through a multistage cluster random sampling in Liyang City, southern Jiangsu Province, China. FINDINGS TO DATE: The Liyang Study included detailed sociodemographic, anthropometric and health-related behaviour, common NCDs and blood sample information. Moreover, the study gathered a series of data on specific scales including the activities of daily living, instrumental activities of daily living, abbreviated mental test, Food Frequency Questionnaire and EuroQol 5-Dimensions 5-Levels Scale. Of the 10 056 participants, 52.92% (n=5322) were female and 92.26% (n=9278) came from rural areas. The mean age was 49.9±16.2 years. Men were more likely to have a higher level of education, annual income and a paid job than women (p<0.05). The top three overall most prevalent NCDs in the study were hypertension (18.06%, n=1815), digestive diseases (7.88%, n=791), and arthritis or rheumatism (5.28%, n=530). Women had a significantly higher prevalence of diabetes (5.46%, n=290 vs 4.42%, n=209, p=0.016) and arthritis (6.04%, n=321 vs 4.42%, n=209, p<0.001) than men, while the opposite was true for chronic lung diseases such as chronic obstructive pulmonary disease (1.37%, n=65 vs 0.92%, n=49, p=0.032) and chronic hepatic diseases (0.80%, n=38 vs 0.47%, n=25, p=0.035). FUTURE PLANS: The current study will give valuable insights into the association between sociodemographic factors, health-related behaviour, diet, cognition, disability and genetic factors and the most prevalent NCDs among local community residents. Starting from 2022, a follow-up survey will be conducted every 3 years to further explore the causal relationship between the above factors and NCDs.

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.

Metabolomics on vascular events and death after acute ischemic stroke: A prospective matched nested case-control study.

BACKGROUND AND AIMS: Ischemic stroke is a major contributor to global mortality and disability. Metabolomics represents a powerful tool for discovering biomarkers of ischemic stroke due to its ability to detect metabolites small enough to cross the blood-brain barrier. The aim of this study is to identify potential metabolic biomarkers for predicting 1-year vascular events and death after acute ischemic stroke (AIS). METHODS: We adopted a nested case-control design from a multicenter prospective cohort study. A total of 143 AIS patients with 1-year vascular events/death and 143 sex-, age- and center-matched patients without 1-year vascular events/death were selected, and further divided into the discovery set (n = 140) and validation set (n = 146). We then performed untargeted metabolomics analysis by liquid chromatography coupled to mass spectrometry. RESULTS: Metabolomics analyses could predict 1-year vascular events and death after AIS using pattern recognition methods. Eight metabolites (e.g., LysoPC(18:1)) were identified as potential biomarkers of AIS prognosis. Four of them (e.g., PS(O-18:0/0:0)) were found to be significantly decreased in patients with early vascular events/death. Adding these metabolic biomarkers to traditional factors resulted in a great improvement of the predictive utility for 1-year vascular events/death, with net reclassification index and integrated discrimination improvement being 0.9143 (p < 0.0001) and 0.1906 (p < 0.0001) in the discovery cohort, and 0.9041 (p < 0.0001) and 0.1896 (p < 0.0001) in the validation cohort. CONCLUSIONS: This study found several metabolic biomarkers for 1-year vascular events and death after AIS, providing opportunities for the construction of prognostic models and the discovery of novel therapeutic targets.

Role of Pulmonary Function in Predicting New-Onset Cardiometabolic Diseases and Cardiometabolic Multimorbidity.

BACKGROUND: Although pulmonary function has been studied in relationship to individual cardiometabolic diseases, uncertainty persists about the difference in risk magnitudes of pulmonary function for these diseases and its association with cardiometabolic multimorbidity (CM). RESEARCH QUESTION: Does pulmonary function have different risk magnitudes for cardiometabolic diseases and CM? STUDY DESIGN AND METHODS: This study used data from the UK Biobank, including 357,433 individuals with no cardiometabolic diseases at baseline (stage I) and 35,034 individuals with one cardiometabolic disease at baseline (stage II). Pulmonary function was measured by FVC or FEV1. We defined CM as the coexistence of at least two cardiometabolic diseases: type 2 diabetes (T2D), coronary heart disease (CHD), and stroke. Multinomial logistic regression models and Cox proportional hazards models were performed to estimate ORs or hazard ratios (HRs) and their 95% CIs for the longitudinal relationship between baseline pulmonary function and incident cardiometabolic outcomes. RESULTS: In stage I, FVC showed the most pronounced associations with new-onset CM and T2D among the four mutually exclusive end points. Compared with the lowest quartile (quartile 1), the adjusted ORs of quartile 4 of FVC were 0.525 (95% CI, 0.468-0.589) for CM, 0.534 (95% CI, 0.498-0.572) for T2D alone, 0.817 (95% CI, 0.751-0.888) for stroke alone, and 0.800 (95% CI, 0.764-0.837) for CHD alone. In stage II, FVC also was associated with the risk of CM in patients with T2D (HR of quartile 4, 0.727; 95% CI, 0.649-0.814), patients with CHD (HR, 0.635; 95% CI, 0.555-0.727), and patients who experienced stroke (HR, 0.783, 95% CI, 0.642-0.955). Similar results were observed for FEV1 in both stages. INTERPRETATION: This study revealed the different risk associations of pulmonary function with individual cardiometabolic diseases and CM. Tailor-made screening and monitoring through pulmonary function may be applicable for the precise prevention and control of these conditions.

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.

Application of Metabolomics to the Discovery of Biomarkers for Ischemic Stroke in the Murine Model: a Comparison with the Clinical Results.

Ischemic stroke (IS) is a major cause of mortality and disability worldwide. However, the pathogenesis of IS remains unknown, and methods for early prediction and diagnosis of IS are lacking. Metabolomics can be applied to biomarker discovery and mechanism exploration of IS by exploring metabolic alterations. In this review, 62 IS metabolomics studies in the murine model published from January 2006 to December 2020 in the PubMed and Web of Science databases were systematically reviewed. Twenty metabolites (e.g., lysine, phenylalanine, methionine, tryptophan, leucine, lactate, serine, N-acetyl-aspartic acid, and glutathione) were reported consistently in more than two-third murine studies. The disturbance of metabolic pathways, such as arginine biosynthesis; alanine, aspartate and glutamate metabolism; aminoacyl-tRNA biosynthesis; and citrate cycle, may be implicated in the development of IS by influencing the biological processes such as energy failure, oxidative stress, apoptosis, and glutamate toxicity. The transient middle cerebral artery occlusion model and permanent middle cerebral artery occlusion model exhibit both common and distinct metabolic patterns. Furthermore, five metabolites (proline, serine, LysoPC (16:0), uric acid, glutamate) in the blood sample and 7 metabolic pathways (e.g., alanine, aspartate, and glutamate metabolism) are shared in animal and clinical studies. The potential biomarkers and related pathways of IS in the murine model may facilitate the biomarker discovery for early diagnosis of IS and the development of novel therapeutic targets.