Circulating proteomic signatures of age are closely associated with aging and age-related diseases; however, the utility of changes in secreted proteins in identifying therapeutic targets for diseases remains unclear. Serum proteomic profiling of an age-stratified healthy population and further community-based cohort together with heart failure patients study demonstrated that circulating C-C motif chemokine ligand 17 (CCL17) level increased with age and correlated with cardiac dysfunction. Subsequent animal experiments further revealed that Ccll7-KO significantly repressed aging and angiotensin II (Ang II)–induced cardiac hypertrophy and fibrosis, accompanied by the plasticity and differentiation of T cell subsets. Furthermore, the therapeutic administration of an anti-CCL17 neutralizing antibody inhibited Ang II–induced pathological cardiac remodeling. Our findings reveal that chemokine CCL17 is identifiable as a novel therapeutic target in age-related and Ang II–induced pathological cardiac hypertrophy and heart failure.

Ischemic stroke (IS), chronic obstructive pulmonary disease (COPD) and diabetes mellitus (DM) account for a large burden of premature deaths. However, few studies have investigated the associations between fine particular matter (PM_2.5) components and mortality of IS, COPD and DM. We aimed to examine these associations in Beijing, China. Data on daily mortality, air pollutants and meteorological factors from 2008 to 2011 in Beijing were collected. Daily concentrations of five PM_2.5 components, namely, sulfate ion (SO₄²⁻), ammonium ion (NH₄⁺), nitrate ion (NO₃⁻), organic matter (OM) and black carbon (BC), were obtained from the Tracking Air Pollution (TAP) database in China. The association between PM_2.5 components and daily deaths was explored using a quasi-Poisson regression with the distributed lag nonlinear model (DLNM). The average daily concentrations of SO₄²⁻, NH₄⁺, NO₃⁻, OM and BC were 11.24, 8.37, 12.00, 17.34 and 3.32 μg/m³, respectively. After adjusting for temperature, relative humidity, pressure, particulate matter less than 10 μm in aerodynamic diameter (PM₁₀), nitrogen dioxide (NO₂) and sulfur dioxide (SO₂), an IQR increase in OM at lag day 2 and lag day 6 was associated with an increased DM mortality risk (RR 1.038; 95% CI: 1.005–1.071) and COPD mortality risk (RR 1.013; 95% CI: 1.001–1.026). An IQR increase in BC at lag day 0 and lag day 6 was associated with increased COPD mortality risk (RR 1.228; 95% CI: 1.017–1.48, RR 1.059; 95% CI: 1.001–1.121). Cumulative exposure to SO₄²⁻ and NH₄⁺ was associated with an increased mortality risk for IS, with the highest effect found for lag of 0–7 days (RR 1.085; 95% CI: 1.010–1.167, RR 1.083; 95% CI: 1.003–1.169). These effects varied by sex and age group. This study demonstrated associations of short-term exposure to PM_2.5 components with increased risk of IS, COPD and DM mortality in the general population. Our study also highlighted susceptible subgroups.

Background: As rare diseases (RDs) receive increasing attention, obtaining accurate RD incidence estimates has become an essential concern in public health. Since RDs are difficult to diagnose, include diverse types, and have scarce cases, traditional epidemiological methods are costly in RD registries. With the development of the internet, users have become accustomed to searching for disease-related information through search engines before seeking medical treatment. Therefore, online search data provide a new source for estimating RD incidences. Objective: The aim of this study was to estimate the incidences of multiple RDs in distinct regions of China with online search data. Methods: Our research scale included 15 RDs in China from 2016 to 2019. The online search data were obtained from Sogou, one of the top 3 commercial search engines in China. By matching to multilevel keywords related to 15 RDs during the 4 years, we retrieved keyword-matched RD-related queries. The queries used before and after the keyword-matched queries formed the basis of the RD-related search sessions. A two-step method was developed to estimate RD incidences with users’ intents conveyed by the sessions. In the first step, a combination of long short-term memory and multilayer perceptron algorithms was used to predict whether the intents of search sessions were RD-concerned, news-concerned, or others. The second step utilized a linear regression (LR) model to estimate the incidences of multiple RDs in distinct regions based on the RD- and news-concerned session numbers. For evaluation, the estimated incidences were compared with RD incidences collected from China’s national multicenter clinical database of RDs. The root mean square error (RMSE) and relative error rate (RER) were used as the evaluation metrics. Results: The RD-related online data included 2,749,257 queries and 1,769,986 sessions from 1,380,186 users from 2016 to 2019. The best LR model with sessions as the input estimated the RD incidences with an RMSE of 0.017 (95% CI 0.016-0.017) and an RER of 0.365 (95% CI 0.341-0.388). The best LR model with queries as input had an RMSE of 0.023 (95% CI 0.017-0.029) and an RER of 0.511 (95% CI 0.377-0.645). Compared with queries, using session intents achieved an error decrease of 28.57% in terms of the RER (P=.01). Analysis of different RDs and regions showed that session input was more suitable for estimating the incidences of most diseases (14 of 15 RDs). Moreover, examples focusing on two RDs showed that news-concerned session intents reflected news of an outbreak and helped correct the overestimation of incidences. Experiments on RD types further indicated that type had no significant influence on the RD estimation task. Conclusions: This work sheds light on a novel method for rapid estimation of RD incidences in the internet era, and demonstrates that search session intents were especially helpful for the estimation. The proposed two-step estimation method could be a valuable supplement to the traditional registry for understanding RDs, planning policies, and allocating medical resources. The utilization of search sessions in disease detection and estimation could be transferred to infoveillance of large-scale epidemics or chronic diseases.

Background: Considerable variability exists in the clinical presentations of pulmonary arterial hypertension (PAH). Greater understanding of the comorbidities observed in Chinese patients with PAH is urgently needed. Methods: This 10-year retrospective analysis was based on clinical data from hospital discharge records for individuals diagnosed with PAH (n = 2584). We used propensity score matching to match patients with PAH to individuals without a PAH diagnosis in a ratio of 1:1, by age, sex, discharge time, and department, over the same period. We constructed multimorbidity networks based on sex and age, and used the cosine index to measure the co-occurrence of chronic diseases. Results: The mean numbers of comorbidities were 4.7 and 3.8 for patients with PAH and controls, respectively. The main central and hub disorders were renal osteodystrophy, cardiovascular illnesses, background retinopathy, diabetes mellitus, systemic lupus erythematosus, epilepsy, and autoimmune hemolytic anemia. The average neighbor degree and closeness were significantly smaller in the multimorbidity networks of patients with PAH than control participants (Kolmogorov–Smirnov test, all P < 0.05). Conclusion: Our findings may aid in preventing comorbidities among patients with PAH and deepening understanding of the underlying physiopathological mechanisms.

BACKGROUND As rare diseases (RDs) receive increasing attention, obtaining accurate RD incidence estimates has become an essential concern in public health. Since RDs are difficult to diagnose, include diverse types, and have scarce cases, traditional epidemiological methods are costly in RD registries. With the development of the internet, users have become accustomed to searching for disease-related information through search engines before seeking medical treatment. Therefore, online search data provide a new source for estimating RD incidences. OBJECTIVE The aim of this study was to estimate the incidences of multiple RDs in distinct regions of China with online search data. METHODS Our research scale included 15 RDs in China from 2016 to 2019. The online search data were obtained from Sogou, one of the top 3 commercial search engines in China. By matching to multilevel keywords related to 15 RDs during the 4 years, we retrieved keyword-matched RD-related queries. The queries used before and after the keyword-matched queries formed the basis of the RD-related search sessions. A two-step method was developed to estimate RD incidences with users’ intents conveyed by the sessions. In the first step, a combination of long short-term memory and multilayer perceptron algorithms was used to predict whether the intents of search sessions were RD-concerned, news-concerned, or others. The second step utilized a linear regression (LR) model to estimate the incidences of multiple RDs in distinct regions based on the RD- and news-concerned session numbers. For evaluation, the estimated incidences were compared with RD incidences collected from China’s national multicenter clinical database of RDs. The root mean square error (RMSE) and relative error rate (RER) were used as the evaluation metrics. RESULTS The RD-related online data included 2,749,257 queries and 1,769,986 sessions from 1,380,186 users from 2016 to 2019. The best LR model with sessions as the input estimated the RD incidences with an RMSE of 0.017 (95% CI 0.016-0.017) and an RER of 0.365 (95% CI 0.341-0.388). The best LR model with queries as input had an RMSE of 0.023 (95% CI 0.017-0.029) and an RER of 0.511 (95% CI 0.377-0.645). Compared with queries, using session intents achieved an error decrease of 28.57% in terms of the RER (P=.01). Analysis of different RDs and regions showed that session input was more suitable for estimating the incidences of most diseases (14 of 15 RDs). Moreover, examples focusing on two RDs showed that news-concerned session intents reflected news of an outbreak and helped correct the overestimation of incidences. Experiments on RD types further indicated that type had no significant influence on the RD estimation task. CONCLUSIONS This work sheds light on a novel method for rapid estimation of RD incidences in the internet era, and demonstrates that search session intents were especially helpful for the estimation. The proposed two-step estimation method could be a valuable supplement to the traditional registry for understanding RDs, planning policies, and allocating medical resources. The utilization of search sessions in disease detection and estimation could be transferred to infoveillance of large-scale epidemics or chronic diseases.