Long-term PM2.5 exposure and peak expiratory flow in middle-aged and older people in China: A quasi-experimental study.

China's Clean Air Act (CCAA) has been demonstrated to reduce the public health burden of ambient air pollution. Few studies have assessed the health effects of CCAA on lung function. We aimed to investigate the effects of CCAA and PM2.5 exposures on peak expiratory flow (PEF) in middle-aged and older people in China. Three waves (2011, 2013, and 2015) of the China Health and Retirement Longitudinal Study (CHARLS) were included in this study. We performed a difference-in-difference (DID) model and mixed effect method to assess the association between CCAA, PM2.5, and PEF. To increase the reliability, multiple environmental factors were considered, and spline function was utilized to fit the spatial autocorrelations. We found that the risk of decreased PEF in the policy intervention group was reduced by 46% (95% CI: 23%~62%). The estimate showed a 10µg/m3 increase in PM2.5 would increase the risk of decreased PEF by 10% (95% CI: 3%~18%). The results of the mixed effect model showed a 10 µg/m3 increase in PM2.5 concentration was associated with a 2.23% (95% CI: 1.35%~3.06%) decrease in the PEF. These results contributed to the limited epidemiology evidence on demonstrating the effect of PM2.5 on lung function.

Association of Performance on Multiple Cognitive Domains with Sarcopenia among Middle-Aged and Older Adults.

INTRODUCTION: The relationship between cognitive function and subsequent sarcopenia remains unclear. Therefore, this study aimed to examine the associations of performance on multiple cognitive domains with sarcopenia in the middle-aged and older adults. METHODS: This longitudinal analysis (wave 2011-2013) included 2,934 participants from the CHARLS study. Sarcopenia was defined by the Asian Sarcopenia Working Group 2019 criteria. Cognitive function was measured by the Chinese version of the Mini-Mental State Examination (MMSE). Three interpretable techniques, namely SHapley Additive exPlanations (SHAP) and two built-in methods (coefficients of logistic regression and Gini importance of random forest), were used to assess the relationship between MMSE, its components (orientation, attention, episodic memory, and visuospatial ability) and sarcopenia. In addition, the association of MMSE score and its components with sarcopenia was further validated using stepwise regression. RESULTS: All interpretable methods showed that MMSE score was important predictors of sarcopenia, especially the SHAP (MMSE score ranked top one). For its components, episodic memory, visuospatial ability, and attention showed high predictive value compared with orientation. Stepwise regression analyses showed that MMSE score and its components of episodic memory and visuospatial ability were correlated with sarcopenia, with their odds ratios of 0.93 (95% CI: 0.91-0.96, p < 0.001), 0.87 (95% CI: 0.82-0.93, p < 0.001), and 1.32 (95% CI: 1.05-1.65, p = 0.016), respectively. CONCLUSIONS: Better cognitive function especially episodic memory and visuospatial ability was negatively associated with incident sarcopenia among community middle-aged and older adults.

Can single disease payment impact hospitalization expenses and quality in district hospital? A case study in Fujian, China.

BACKGROUND: China is exploring payment reform methods for patients to address the escalating issue of increasing medical costs. While most district hospitals were still in the stage of Single Disease Payment (SDP) due to conditions, there is a scarcity of research on comprehensive assessment of SDP. This study aims to evaluate the implementation of SDP in a district hospital, and provided data support and scientific reference for improving SDP method and accelerating medical insurance payment reform at district hospitals. METHODS: Data was collected from 2337 inpatient medical records at a district hospital in Fuzhou, China from 2016 to 2021. These diagnoses principally included type 2 diabetes, planned cesarean sections, and lacunar infarction. Structural variation analysis was conducted to examine changes in the internal cost structure and dynamic shifts in medical expenses for both the insured (treatment group) and uninsured (control group) patients, pre- and post-implementation of the SDP policy on August 1, 2018. The difference-in-differences (DID) method was employed to assess changes in hospitalization expenses and quality indicators pre- and post-implementation. Furthermore, subjective evaluation of medical quality was enhanced through questionnaire surveys with 181 patients and 138 medical staff members. RESULTS: The implementation of SDP decreased the medical expenses decreased significantly (P < 0.05), which can also optimize the cost structure. The drug cost ratio descended significantly, and the proportion of laboratory fee rose slightly. The changes in infection rate, cure rate, and length of stay indicated enhanced medical quality (P < 0.05). The satisfaction of inpatients with SDP was high (89.2%). Medical staff expressed an upper middle level of satisfaction (77.2%) but identified difficulties with the implementation such as "insufficient coverage of disease types". CONCLUSION: After the implementation of SDP in district hospitals, considerable progress has been achieved in restraining medical expenses, coupled with notable enhancements in both medical quality and patient satisfaction levels. However, challenges persist regarding cost structure optimization and underutilization of medical resources. This study suggests that district hospitals can expedite insurance payment reform by optimizing drug procurement policies, sharing examination information, and strengthening the management of medical records.

Associations of visual, hearing, and dual sensory impairment with motoric cognitive risk syndrome: Observational and Mendelian randomization analyses.

BACKGROUND: Dementia is associated with individual vision impairment (VI) and hearing impairment (HI). However, little is known about their associations with motoric cognitive risk syndrome (MCR), a pre-dementia stage. We investigated the association of VI, HI, and dual sensory impairment (DSI) with MCR and to further evaluate causal relationships using Mendelian randomization (MR) approach. METHODS: First, an observational study was conducted in the China Health and Retirement Longitudinal Study (CHARLS). Evaluate the cross-sectional and longitudinal associations of VI, HI, and DSI with MCR using the logistic regression models and Cox proportional hazard models, respectively. Second, evaluate the causal association between VI and HI with MCR using MR analysis. The GWAS data was used for genetic instruments, including 88,250 of European ancestry (43,877 cases and 44,373 controls) and 504,307 with "white British" ancestry (100,234 cases and 404,073 controls), respectively; MCR information was obtained from the GWAS with 22,593 individuals. Inverse variance weighted was the primary method and sensitivity analysis was used to evaluate the robustness of MR methods. RESULTS: In the observational study, VI (HR: 1.767, 95%CI: 1.331-2.346; p < 0.001), HI (HR: 1.461, 95%CI: 1.196-1.783; p < 0.001), and DSI (HR: 1.507, 95%CI: 1.245-1.823; p < 0.001) were significantly associated with increased risk of MCR. For the MR, no causal relationship between VI (OR: 0.902, 95% CI: 0.593-1.372; p = 0.631) and HI (OR: 1.016, 95% CI: 0.989-1.043; p = 0.248) with MCR risk, which is consistent with the sensitivity analysis. CONCLUSION: VI, HI, and DSI were significantly associated with MCR, but MR analysis failed to provide evidence of their causal relationship. Emphasized the importance of sensory impairment screening in identifying high-risk populations for dementia.

Long-term effect of extreme temperature on cognitive function of middle-aged and older adults in China.

BACKGROUND: Several studies have explored the association between temperature and cognitive function. However, few studies have examined the effect of extreme temperature on cognitive function. In this study, we aimed to quantify the long-term effect of extreme temperature (e.g., heat waves, cold spells, and hot night excess (HNE)) on cognitive performance in middle-aged and older people in China. METHOD: We investigated 7915 aged >45 years people from the China Health and Retirement Longitudinal Study (CHARLS), surveyed in 2011 and 2015. A structured questionnaire was utilized to assess cognitive function, including four dimensions: episodic memory, attention, orientation, and visuo-construction. Hourly ambient temperature from the ERA5-Land datasets were used to calculate variables indicating extreme temperature. We performed difference-in-difference (DID) models to assess the potential causal relationship between extreme temperature and cognitive function. RESULTS: Non-linear analyses suggested that both sustained increases in temperature and excessive variability in temperature increased the risk of cognitive decline. Meanwhile, we observed the extra risk of global cognitive function decline was 2.3% (95% Confidence interval (95% CI): 0.2%, 4.4%) for heat waves (one unit increase) and 5.9% (95% CI: 0.6%, 11.6%) for HNE (one unit increase), while the association for cold spells was insignificant. Two cognitive dimensions, episodic memory and visuo-construction, were sensitive to these two heat-related factors. CONCLUSION: Extreme temperature was inversely related to cognitive performance in middle-aged and older adults, which was substantial for heat waves and HNE particularly. The effect size varied by cognitive dimensions.

Blood biomarkers in dynamic prediction of conversion to Alzheimer's disease: An application of joint modeling.

OBJECTIVES: To investigate the accuracy of longitudinal trajectories of blood biomarkers for predicting future onset of AD among MCI participants as well as to demonstrate dynamic prediction of the individual conversion risk applying joint modeling. METHODS: A total of 446 participants with MCI at baseline from the Alzheimer's Disease Neuroimaging Initiative database were included. We introduced joint modeling to analyze the effects of the longitudinal blood biomarkers on the conversion risk to AD, and further to build individual-specific prediction risk model. RESULTS: During the follow-up, 345 participants remained with MCI and 101 progressed to AD, and were categorized as non-progression and progression group, respectively. Longitudinally, the positive association of the concentration dynamics of plasma p-tau181 and NfL with the conversion risk to AD from MCI was also demonstrated, with Hazard Ratio (HR) = 5.83 and HR = 4.18, respectively. When incorporating plasma p-tau181 and NfL together to predict AD progression, we observed improved performance (AUC = 0.701, Brier Score = 0.119). Two participants were chosen to exemplify the individual-specific risk prediction at different follow-up time for comparative analysis. CONCLUSIONS: Plasma p-tau181 and NfL could serve as biomarkers for the prediction of AD onset, and the individualized prediction opens up the possibility to provide clinical information at a personal level.

Pathogenic variations in MAML2 and MAMLD1 contribute to congenital hypothyroidism due to dyshormonogenesis by regulating the Notch signalling pathway.

BACKGROUND: In several countries, thyroid dyshormonogenesis is more common than thyroid dysgenesis in patients with congenital hypothyroidism (CH). However, known pathogenic genes are limited to those directly involved in hormone biosynthesis. The aetiology and pathogenesis of thyroid dyshormonogenesis remain unknown in many patients. METHODS: To identify additional candidate pathogenetic genes, we performed next-generation sequencing in 538 patients with CH and then confirmed the functions of the identified genes in vitro using HEK293T and Nthy-ori 3.1 cells, and in vivo using zebrafish and mouse model organisms. RESULTS: We identified one pathogenic MAML2 variant and two pathogenic MAMLD1 variants that downregulated canonical Notch signalling in three patients with CH. Zebrafish and mice treated with N-[N-(3,5-difluorophenacetyl)-l-alanyl]-S-phenylglycine t-butylester, a γ-secretase inhibitor exhibited clinical manifestations of hypothyroidism and thyroid dyshormonogenesis. Through organoid culture of primary mouse thyroid cells and transcriptome sequencing, we demonstrated that Notch signalling within thyroid cells directly affects thyroid hormone biosynthesis rather than follicular formation. Additionally, these three variants blocked the expression of genes associated with thyroid hormone biosynthesis, which was restored by HES1 expression. The MAML2 variant exerted a dominant-negative effect on both the canonical pathway and thyroid hormone biosynthesis. MAMLD1 also regulated hormone biosynthesis through the expression of HES3, the target gene of the non-canonical pathway. CONCLUSIONS: This study identified three mastermind-like family gene variants in CH and revealed that both canonical and non-canonical Notch signalling affected thyroid hormone biosynthesis.

Blood Profiles of Community-Dwelling People with Sarcopenia: Analysis Based on the China Health and Retirement Longitudinal Study.

INTRODUCTION: Routine blood factors can be economical and easily accessible candidates for sarcopenia screening and monitoring. The associations between sarcopenia and routine blood factors remain unclear. This study aimed to examine sarcopenia and blood factor associations based on a nation-wide cohort in China. METHODS: A total of 1,307 participants and 17 routine blood indices were selected from two waves (year 2011 and year 2015) of the China Health and Retirement Longitudinal Study (CHARLS). The diagnosis of sarcopenia was based on the criteria proposed by the Asian Working Group for Sarcopenia (AWGS 2019). Generalized mixed-effects models were performed for association analyses. A logistic regression (LR) model was conducted to examine the predictive power of identifying significant blood factors for sarcopenia. RESULTS: A higher sarcopenia risk was cross-sectionally associated with elevated blood concentrations of high-sensitivity C-reactive protein (hsCRP) (OR = 1.030, 95% CI [1.007, 1.053]), glycated hemoglobin (HbA1c) (OR = 1.407, 95% CI [1.126, 1.758]) and blood urea nitrogen (BUN) (OR = 1.044, 95% CI [1.002, 1.089]), and a decreased level of glucose (OR = 0.988, 95% CI [0.979, 0.997]). A higher baseline hsCRP value (OR = 1.034, 95% CI [1.029, 1.039]) and a greater over time change in hsCRP within 4 years (OR = 1.034, 95% CI [1.029, 1.039]) were associated with a higher sarcopenia risk. A higher BUN baseline value was related to a decreased sarcopenia risk over time (OR = 0.981, 95% CI [0.976, 0.986]), while a greater over time changes in BUN (OR = 1.034, 95% CI [1.029, 1.040]) and a smaller over time change in glucose (OR = 0.992, 95% CI [0.984, 0.999]) within 4 years were also related to a higher sarcopenia risk. LR based on significant blood factors (i.e., hsCRP, HbA1c, BUN, and glucose), and sarcopenia status in year 2015 yielded an area under the curve of 0.859 (95% CI: 0.836-0.882). CONCLUSION: Routine blood factors involved in inflammation, protein metabolism, and glucose metabolism are significantly associated with sarcopenia. In clinical practice, plasma hsCRP, BUN, blood sugar levels, sex, age, marital status, height, and weight might be helpful for sarcopenia evaluation and monitoring.

Bridging the gap: coordinating equity and efficiency in older people care resource allocation in China.

BACKGROUND: With the increasing global aging population, how to allocate older people care resources reasonably has become an increasingly urgent international issue. China, as the largest developing country, has made many efforts to actively respond to the challenges of an aging population. However, there are still problems with uneven allocation of older people care resources and low efficiency of allocation. Therefore, this study evaluates the regional differences and dynamic evolution of the equity and efficiency of older people care resource allocation in China from 2009 to 2020, and explores ways to change the current situation. METHODS: The data used in this study were derived from the "China Statistical Yearbook" and the "China Civil Affairs Statistical Yearbook" for the period of 2010-2021. Firstly, the equity of older people care resource allocation was measured using the Gini coefficient, the Theil index, the Older People Care Resource Density Index, and the Older People Care Resource Agglomeration Degree. Secondly, the dynamic Slack-Based Measure data envelopment analysis method was adopted to evaluate efficiency. Lastly, the Z-score is used to normalize the equity index and perform classification matching with the efficiency value. Spatial autocorrelation analysis and hotspot analysis were conducted using GIS technology to examine the dynamic evolution process of older people care resource allocation equity and efficiency, as well as their spatial distribution patterns and coordination across provinces from 2009 to 2020. RESULTS: The equity analysis showed that the spatial distribution of various types of older people care resources was uneven, and the differences were mainly due to internal differences within each region, with the largest equity differences observed in western provinces. Currently, older people care resources are mainly concentrated in eastern regions, while the total amount of older people care resources in western regions and some central regions is relatively small, which cannot meet the older people care needs of residents. The efficiency analysis results showed that the efficiency of older people care resource allocation has been improving over the past 12 years, and in 2020, 77.42% of provinces were located on the efficiency frontier with an average efficiency value of 0.9396. Finally, the coordination analysis results showed that there were significant spatiotemporal differences in the equity and efficiency of older people care resources allocation. CONCLUSION: With the development of society and economy, the total amount and service capacity of older people care resources in China have greatly improved. However, there are still significant spatiotemporal differences in the equity and efficiency of older people care resource allocation. The development of older people care services in central and eastern provinces is unbalanced, and there is a polarization trend in terms of equity and efficiency of older people care resource allocation. Most provinces in western regions face the dual dilemma of inadequate older people care resources and low utilization efficiency. It is recommended that policymakers comprehensively consider population and geographic factors in different provinces, establish relevant allocation standards according to local conditions, improve the redistribution system, and focus on increasing the total amount of older people care resources in underdeveloped provinces while promoting resource flow.

Spatial-temporal distribution patterns and influencing factors analysis of comorbidity prevalence of chronic diseases among middle-aged and elderly people in China: focusing on exposure to ambient fine particulate matter (PM2.5).

OBJECTIVE: This study describes regional differences and dynamic changes in the prevalence of comorbidities among middle-aged and elderly people with chronic diseases (PCMC) in China from 2011-2018, and explores distribution patterns and the relationship between PM2.5 and PCMC, aiming to provide data support for regional prevention and control measures for chronic disease comorbidities in China. METHODS: This study utilized CHARLS follow-up data for ≥ 45-year-old individuals from 2011, 2013, 2015, and 2018 as research subjects. Missing values were filled using the random forest machine learning method. PCMC spatial clustering investigated using spatial autocorrelation methods. The relationship between macro factors and PCMC was examined using Geographically and Temporally Weighted Regression, Ordinary Linear Regression, and Geographically Weighted Regression. RESULTS: PCMC in China showing a decreasing trend. Hotspots of PCMC appeared mainly in western and northern provinces, while cold spots were in southeastern coastal provinces. PM2.5 content was a risk factor for PCMC, the range of influence expanded from the southeastern coastal areas to inland areas, and the magnitude of influence decreased from the southeastern coastal areas to inland areas. CONCLUSION: PM2.5 content, as a risk factor, should be given special attention, taking into account regional factors. In the future, policy-makers should develop stricter air pollution control policies based on different regional economic, demographic, and geographic factors, while promoting public education, increasing public transportation, and urban green coverage.

Development of interpretable machine learning models associated with environmental chemicals to predict all-cause and specific-cause mortality：A longitudinal study based on NHANES.

Limited information is available on potential predictive value of environmental chemicals for mortality. Our study aimed to investigate the associations between 43 of 8 classes representative environmental chemicals in serum/urine and mortality, and further develop the interpretable machine learning models associated with environmental chemicals to predict mortality. A total of 1602 participants were included from the National Health and Nutrition Examination Survey (NHANES). During 154,646 person-months of follow-up, 127 deaths occurred. We found that machine learning showed promise in predicting mortality. CoxPH was selected as the optimal model for predicting all-cause mortality with time-dependent AUROC of 0.953 (95%CI: 0.951-0.955). Coxnet was the best model for predicting cardiovascular disease (CVD) and cancer mortality with time-dependent AUROCs of 0.935 (95%CI: 0.933-0.936) and 0.850 (95%CI: 0.844-0.857). Based on clinical variables, adding environmental chemicals could enhance the predictive ability of cancer mortality (P < 0.05). Some environmental chemicals contributed more to the models than traditional clinical variables. Combined the results of association and prediction models by interpretable machine learning analyses, we found urinary methyl paraben (MP) and urinary 2-napthol (2-NAP) were negatively associated with all-cause mortality, while serum cadmium (Cd) was positively associated with all-cause mortality. Urinary bisphenol A (BPA) was positively associated with CVD mortality.

LPS-induced senescence of macrophages aggravates calcification and senescence of vascular smooth muscle cells via IFITM3.

BACKGROUND: Cellular senescence, macrophages infiltration, and vascular smooth muscle cells (VSMCs) osteogenic transdifferentiation participate in the pathophysiology of vascular calcification in chronic kidney disease (CKD). Senescent macrophages are involved in the regulation of inflammation in pathological diseases. In addition, senescent cells spread senescence to neighboring cells via Interferon-induced transmembrane protein3 (IFITM3). However, the role of senescent macrophages and IFITM3 in VSMCs calcification remains unexplored. AIMS: To explore the hypothesis that senescent macrophages contribute to the calcification and senescence of VSMCs via IFITM3. METHODS: Here, the macrophage senescence model was established using Lipopolysaccharides (LPS). The VSMCs were subjected to supernatants from macrophages (MCFS) or LPS-induced macrophages (LPS-MCFS) in the presence or absence of calcifying media (CM). Senescence-associated ß-galactosidase (SA-ß-gal), Alizarin red (AR), immunofluorescent staining, and western blot were used to identify cell senescence and calcification. RESULTS: The expression of IFITM3 was significantly increased in LPS-induced macrophages and the supernatants. The VSMCs transdifferentiated into osteogenic phenotype, expressing higher osteogenic differentiation markers (RUNX2) and lower VSMCs constructive makers (SM22α) when cultured with senescent macrophages supernatants. Also, senescence markers (p16 and p21) in VSMCs were significantly increased by senescent macrophages supernatants treated. However, IFITM3 knockdown inhibited this process. CONCLUSIONS: Our study showed that LPS-induced senescence of macrophages accelerated the calcification of VSMCs via IFITM3. These data provide a new perspective linking VC and aging, which may provide clues for diagnosing and treating accelerated vascular aging in patients with CKD.

The Association Between Allostatic Load and Frailty Trajectories Among Adults Aged 50+ Years: Mediating Role of Physical Activity.

OBJECTIVES: To identify frailty trajectories and examine its association with allostatic load (AL) and mediating effect of physical activity (PA). METHODS: This study included 8,082 adults from the English Longitudinal Study of Aging over Waves 4-9. AL was calculated by 14 biological indicators, and a 53-item frailty index was used to evaluate frailty. Frailty trajectories were classified by group-based trajectory modeling, and the mediated effect of PA was tested by causal mediation analysis. RESULTS: Four frailty trajectories were identified: "Robustness" (n = 4,437, 54.9%), "Incident prefrailty" (n = 2,061, 25.5%), "Prefrailty to frailty" (n = 1,136, 14.1%), and "Frailty to severe frailty" (n = 448, 5.5%). High baseline AL was associated with increased odds of "Incident prefrailty," "Prefrailty to frailty," and "Frailty to severe frailty" trajectories. PA demonstrated significant mediated effects in aforementioned associations. CONCLUSIONS: AL is significantly associated with the onset and progression of frailty, and such associations are partially mediated by PA.

Blap-6, a Novel Antifungal Peptide from the Chinese Medicinal Beetle Blaps rhynchopetera against Cryptococcus neoformans.

Cryptococcus neoformans (C. neoformans) is a pathogenic fungus that can cause life-threatening meningitis, particularly in individuals with compromised immune systems. The current standard treatment involves the combination of amphotericin B and azole drugs, but this regimen often leads to inevitable toxicity in patients. Therefore, there is an urgent need to develop new antifungal drugs with improved safety profiles. We screened antimicrobial peptides from the hemolymph transcriptome of Blaps rhynchopetera (B. rhynchopetera), a folk Chinese medicine. We found an antimicrobial peptide named blap-6 that exhibited potent activity against bacteria and fungi. Blap-6 is composed of 17 amino acids (KRCRFRIYRWGFPRRRF), and it has excellent antifungal activity against C. neoformans, with a minimum inhibitory concentration (MIC) of 0.81 µM. Blap-6 exhibits strong antifungal kinetic characteristics. Mechanistic studies revealed that blap-6 exerts its antifungal activity by penetrating and disrupting the integrity of the fungal cell membrane. In addition to its direct antifungal effect, blap-6 showed strong biofilm inhibition and scavenging activity. Notably, the peptide exhibited low hemolytic and cytotoxicity to human cells and may be a potential candidate antimicrobial drug for fungal infection caused by C. neoformans.

Interpretable prediction models for disability in older adults with hypertension: the Chinese Longitudinal Healthy Longevity and Happy Family Study.

BACKGROUND: Older adults with hypertension have a high risk of disability, while an accurate risk prediction model is still lacking. This study aimed to construct interpretable disability prediction models for older Chinese with hypertension based on multiple time intervals. METHODS: Data were collected from the Chinese Longitudinal Healthy Longevity and Happy Family Study for 2008-2018. A total of 1602, 1108, and 537 older adults were included for the periods of 2008-2012, 2008-2014, and 2008-2018, respectively. Disability was measured by basic activities of daily living. Least absolute shrinkage and selection operator (LASSO) was applied for feature selection. Five machine learning algorithms combined with LASSO set and full-variable set were used to predict 4-, 6-, and 10-year disability risk, respectively. Area under the receiver operating characteristic curve was used as the main metric for selection of the optimal model. SHapley Additive exPlanations (SHAP) was used to explore important predictors of the optimal model. RESULTS: Random forest in full-variable set and XGBoost in LASSO set were the optimal models for 4-year prediction. Support vector machine was the optimal model for 6-year prediction on both sets. For 10-year prediction, deep neural network in full variable set and logistic regression in LASSO set were optimal models. Age ranked the most important predictor. Marital status, body mass index, score of Mini-Mental State Examination, and psychological well-being score were also important predictors. CONCLUSIONS: Machine learning shows promise in screening out older adults at high risk of disability. Disability prevention strategies should specifically focus on older patients with unfortunate marriage, high BMI, and poor cognitive and psychological conditions.

Large-scale forward genetic screening of zebrafish affecting thyroid development.

The thyroid follicular cells originate from the foregut endoderm and elucidating which genes and signaling pathways regulate their development is crucial for understanding developmental disorders as well as diseases in adulthood. We exploited unique advantages of the zebrafish model to carry an ENU-based forward mutagenesis screen aiming at identifying genes involved in the development and function of the thyroid follicular cells. ENU is an excellent chemical mutagen due to its high mutation efficiency and an indiscriminate selection of genes. A total of 1606 F2 families from 36 ENU treated founders was raised and embryos from F3 generation were collected at 5dpf to perform the whole embryo in situ hybridization with a cocktail probe of thyroid marker thyroglobulin(tg), pituitary marker thyroid stimulating hormone (tshba) to determine the mutagenic phenotype. Among the 1606 F2 families, 112 F2 mutant families with normal development stages except for thyroid dysfunction were identified and divided into three different groups according to their phenotypic characteristics. Further studies of the mutants are likely to shed more insights into the molecular basis of both the thyroid development and function in the zebrafish and vertebrate.

Chronic pain increases the risk of motoric cognitive risk syndrome at 4 years of follow-up: evidence from the China Health and Retirement Longitudinal Study.

BACKGROUND AND PURPOSE: Slower gait speed and subjective cognitive concerns are characteristics of the motoric cognitive risk (MCR) syndrome. This study aimed to examine if changes in pain may be hallmarks of early MCR, through investigating the magnitude of the association of chronic pain and the risk of MCR at 4 years follow-up. METHODS: In total, 3711 participants without dementia or any mobility disability aged ≥60 years were studied, including 1413 with chronic pain, enrolled in the China Health and Retirement Longitudinal Study, a prospective cohort study. MCR assessed at wave 1 (2011) and wave 3 (2015) was used as the exposure. Cox regression analysis was used to examine the longitudinal association between chronic pain and MCR after adjusting for individual factors, behaviors/physiology factors and societal factors. Four years later, the incident MCR was evaluated. RESULTS: After adjusting for individual factors, chronic pain was found to increase the risk of MCR development over time by about 1.5 times (hazard ratio 1.562, 95% confidence interval 1.228-1.986; p < 0.001) and to be linked with incident MCR at baseline (odds ratio 1.397, 95% confidence interval 1.149-1.698; p < 0.001). These associations remained substantial when behaviors/physiology factors and societal factors were taken into account in the analytical models. CONCLUSIONS: The findings of our study imply that incident MCR may be exacerbated by chronic pain. Further exploration is required to find out whether chronic pain is a modifiable risk factor for MCR.

Predicting Alzheimer's Disease with Interpretable Machine Learning.

INTRODUCTION: This study aimed to develop novel machine learning models for predicting Alzheimer's disease (AD) and identify key factors for targeted prevention. METHODS: We included 1,219, 863, and 482 participants aged 60+ years with only sociodemographic, both sociodemographic and self-reported health, both the former two and blood biomarkers information from Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Machine learning models were constructed for predicting the risk of AD for the above three populations. Model performance was evaluated by discrimination, calibration, and clinical usefulness. SHapley Additive exPlanation (SHAP) was applied to identify key predictors of optimal models. RESULTS: The mean age was 73.49, 74.52, and 74.29 years for the three populations, respectively. Models with sociodemographic information and models with both sociodemographic and self-reported health information showed modest performance. For models with sociodemographic, self-reported health, and blood biomarker information, their overall performance improved substantially, specifically, logistic regression performed best, with an AUC value of 0.818. Blood biomarkers of ptau protein and plasma neurofilament light, age, blood tau protein, and education level were top five significant predictors. In addition, taurine, inosine, xanthine, marital status, and L.Glutamine also showed importance to AD prediction. CONCLUSION: Interpretable machine learning showed promise in screening high-risk AD individual and could further identify key predictors for targeted prevention.

Expenditure projections for community home-based care services for older adults with functional decline in China.

INTRODUCTION: Difficulty in identifying the functional status of older adults creates an imbalance between the supply and demand for community home-based care. Using a multi-level functional classification system to guide care cost measurement may optimize care resources and meet diverse eldercare demands. METHODS: The Markov model was used to project the older population size in different functional decline (FD) statuses. The project cost and the man-hour costing method were combined to forecast the cost of community home-based care for older adults with FD. RESULTS: The projected cost of eldercare increased from 1668.623 billion yuan in 2020 to 2836.754 billion yuan in 2035. By 2035, the total cost for community-based home care for those in pathological development of FD statuses such as "viability disorder," "acute disease," "somatic functional disorder," and "sub-disorder" was projected to be 1094.591 billion, 433.855 billion, 1256.236 billion, and 52.072 billion yuan, respectively, which is 1.24, 1.58, 1.78, and 0.49 times higher than the results by the man-hour costing method. Family caregiving costs are about three times those of professional caregivers. CONCLUSION: The escalating cost of providing graded care for older adults, particularly by family caregivers, presenting a significant evidence for the need to optimize resource allocation and develop a robust human resources plan for community home-based care.

Investigating predictors of progression from mild cognitive impairment to Alzheimer's disease based on different time intervals.

BACKGROUND: Mild cognitive impairment (MCI) is the early stage of AD, and about 10-12% of MCI patients will progress to AD every year. At present, there are no effective markers for the early diagnosis of whether MCI patients will progress to AD. This study aimed to develop machine learning-based models for predicting the progression from MCI to AD within 3 years, to assist in screening and prevention of high-risk populations. METHODS: Data were collected from the Alzheimer's Disease Neuroimaging Initiative, a representative sample of cognitive impairment population. Machine learning models were applied to predict the progression from MCI to AD, using demographic, neuropsychological test and MRI-related biomarkers. Data were divided into training (56%), validation (14%) and test sets (30%). AUC (area under ROC curve) was used as the main evaluation metric. Key predictors were ranked utilising their importance. RESULTS: The AdaBoost model based on logistic regression achieved the best performance (AUC: 0.98) in 0-6 month prediction. Scores from the Functional Activities Questionnaire, Modified Preclinical Alzheimer Cognitive Composite with Trails test and ADAS11 (Unweighted sum of 11 items from The Alzheimer's Disease Assessment Scale-Cognitive Subscale) were key predictors. CONCLUSION: Through machine learning, neuropsychological tests and MRI-related markers could accurately predict the progression from MCI to AD, especially in a short period time. This is of great significance for clinical staff to screen and diagnose AD, and to intervene and treat high-risk MCI patients early.