Shared genetic aetiology of Alzheimer's disease and age-related macular degeneration by APOC1 and APOE genes.

Background: Alzheimer's disease (AD) and age-related macular degeneration (AMD) share similar pathological features, suggesting common genetic aetiologies between the two. Investigating gene associations between AD and AMD may provide useful insights into the underlying pathogenesis and inform integrated prevention and treatment for both diseases. Methods: A stratified quantile-quantile (QQ) plot was constructed to detect the pleiotropy among AD and AMD based on genome-wide association studies data from 17 008 patients with AD and 30 178 patients with AMD. A Bayesian conditional false discovery rate-based (cFDR) method was used to identify pleiotropic genes. UK Biobank was used to verify the pleiotropy analysis. Biological network and enrichment analysis were conducted to explain the biological reason for pleiotropy phenomena. A diagnostic test based on gene expression data was used to predict biomarkers for AD and AMD based on pleiotropic genes and their regulators. Results: Significant pleiotropy was found between AD and AMD (significant leftward shift on QQ plots). APOC1 and APOE were identified as pleiotropic genes for AD-AMD (cFDR <0.01). Network analysis revealed that APOC1 and APOE occupied borderline positions on the gene co-expression networks. Both APOC1 and APOE genes were enriched on the herpes simplex virus 1 infection pathway. Further, machine learning-based diagnostic tests identified that APOC1, APOE (areas under the curve (AUCs) >0.65) and their upstream regulators, especially ZNF131, ADNP2 and HINFP, could be potential biomarkers for both AD and AMD (AUCs >0.8). Conclusion: In this study, we confirmed the genetic pleiotropy between AD and AMD and identified APOC1 and APOE as pleiotropic genes. Further, the integration of multiomics data identified ZNF131, ADNP2 and HINFP as novel diagnostic biomarkers for AD and AMD.

Exploring multimorbidity profiles in middle-aged inpatients: a network-based comparative study of China and the United Kingdom.

BACKGROUND: Multimorbidity is better prevented in younger ages than in older ages. This study aims to identify the differences in comorbidity patterns in middle-aged inpatients from China and the United Kingdom (UK). METHODS: We utilized 184,133 and 180,497 baseline hospitalization records in middle-aged populations (40-59 years) from Shaanxi, China, and UK Biobank. Logistic regression was used to calculate odds ratios and P values for 43,110 unique comorbidity patterns in Chinese inpatients and 21,026 unique comorbidity patterns in UK inpatients. We included the statistically significant (P values adjusted by Bonferroni correction) and common comorbidity patterns (the pattern with prevalence > 1/10,000 in each dataset) and employed network analysis to construct multimorbidity networks and compare feature differences in multimorbidity networks for Chinese and UK inpatients, respectively. We defined hub diseases as diseases having the top 10 highest number of unique comorbidity patterns in the multimorbidity network. RESULTS: We reported that 57.12% of Chinese inpatients had multimorbidity, substantially higher than 30.39% of UK inpatients. The complete multimorbidity network for Chinese inpatients consisted of 1367 comorbidities of 341 diseases and was 2.93 × more complex than that of 467 comorbidities of 215 diseases in the UK. In males, the complexity of the multimorbidity network in China was 2.69 × more than their UK counterparts, while the ratio was 2.63 × in females. Comorbidities associated with hub diseases represented 68.26% of comorbidity frequencies in the complete multimorbidity network in Chinese inpatients and 55.61% in UK inpatients. Essential hypertension, dyslipidemia, type 2 diabetes mellitus, and gastritis and duodenitis were the hub diseases in both populations. The Chinese inpatients consistently demonstrated a higher frequency of comorbidities related to circulatory and endocrine/nutritional/metabolic diseases. In the UK, aside from these comorbidities, comorbidities related to digestive and genitourinary diseases were also prevalent, particularly the latter among female inpatients. CONCLUSIONS: Chinese inpatients exhibit higher multimorbidity prevalence and more complex networks compared to their UK counterparts. Multimorbidity with circulatory and endocrine/nutritional/metabolic diseases among both Chinese and UK inpatients necessitates tailored surveillance, prevention, and intervention approaches. Targeted interventions for digestive and genitourinary diseases are warranted for the UK.

An environment-wide association study for the identification of non-invasive factors for type 2 diabetes mellitus: Analysis based on the Henan Rural Cohort study.

AIM: To explore the influencing factors of Type 2 diabetes mellitus (T2DM) in the rural population of Henan Province and evaluate the predictive ability of non-invasive factors to T2DM. METHODS: A total of 30,020 participants from the Henan Rural Cohort Study in China were included in this study. The dataset was randomly divided into a training set and a testing set with a 50:50 split for validation purposes. We used logistic regression analysis to investigate the association between 56 factors and T2DM in the training set (false discovery rate < 5 %) and significant factors were further validated in the testing set (P < 0.05). Gradient Boosting Machine (GBM) model was used to determine the ability of the non-invasive variables to classify T2DM individuals accurately and the importance ranking of these variables. RESULTS: The overall population prevalence of T2DM was 9.10 %. After adjusting for age, sex, educational level, marital status, and body measure index (BMI), we identified 13 non-invasive variables and 6 blood biochemical indexes associated with T2DM in the training and testing dataset. The top three factors according to the GBM importance ranking were pulse pressure (PP), urine glucose (UGLU), and waist-to-hip ratio (WHR). The GBM model achieved a receiver operating characteristic (AUC) curve of 0.837 with non-invasive variables and 0.847 for the full model. CONCLUSIONS: Our findings demonstrate that non-invasive variables that can be easily measured and quickly obtained may be used to predict T2DM risk in rural populations in Henan Province.

Disease Burden and the Accumulation of Multimorbidity of Noncommunicable Diseases in a Rural Population in Henan, China: Cross-sectional Study.

BACKGROUND: Multimorbidity causes substantial disease and economic burdens on individuals and the health care system. OBJECTIVE: This study aimed to explore the disease burden of multimorbidity and the potential correlations among chronic noncommunicable diseases (NCDs) in a rural population in Henan, China. METHODS: A cross-sectional analysis was performed using the baseline survey of the Henan Rural Cohort Study. Multimorbidity was defined as the simultaneous occurrence of at least two NCDs in a participant. This study examined the multimorbidity pattern of 6 NCDs, including hypertension, dyslipidemia, type 2 diabetes mellitus, coronary heart disease, stroke, and hyperuricemia. RESULTS: From July 2015 to September 2017, a total of 38,807 participants (aged 18-79 years; 15,354 men and 23,453 women) were included in this study. The overall population prevalence of multimorbidity was 28.1% (10,899/38,807), and the multimorbidity of hypertension and dyslipidemia was the most common (8.1%, 3153/38,807). Aging, higher BMI, and unfavorable lifestyles were significantly associated with a higher risk of multimorbidity (multinomial logistic regression, all P<.05). The analysis of the mean age at diagnosis suggested a cascade of interrelated NCDs and their accumulation over time. Compared with participants without 2 conditional NCDs, participants with 1 conditional NCD would have higher odds of another NCD (1.2-2.5; all P<.05), and those with 2 conditional NCDs would elevate the odds of the third NCD to 1.4-3.5 (binary logistic regression, all P<.05). CONCLUSIONS: Our findings indicate a plausible tendency for the coexistence and accumulation of NCDs in a rural population in Henan, China. Early prevention of multimorbidity is essential to reduce the NCD burden in the rural population.

An Observational Study on HIV and Syphilis Rates and Associated Risk Factors among Elderly Men in Wuxi, China.

BACKGROUND: Acquired Immune Deficiency Syndrome (AIDS) remains a nationwide health problem in China; there were a reported 1,045,000 people living with Human immunodeficiency virus (HIV)/AIDS by the end of October 2020, and the proportion of individuals aged 50 years and older living with HIV has also increased from 8% to 24% over the past two decades. METHODS: A cross-sectional study and an 1:2 matched case-control study were conducted from July to August 2016, in Wuxi city, eastern China. A total of 1,000 men aged 50 years and older completed a face-to-face interview regarding their AIDS-related knowledge and attitudes, as well as risk behaviors. RESULTS: Prevalence was 0.1% for HIV and 2% for syphilis. The awareness rate of AIDS-related knowledge among elderly men was 48.9% (range 40.7%-63.9%). The 1꞉2 matched case-control study indicated that only the AIDS-related attitudes were different between the two groups (χ2=8.726, P=0.013), the conditional logistic regression analysis indicated that scores of AIDS health knowledge were the only significant prognostic factor for the infection (HR=0.754 (0.569- 0.999), P=0.049). CONCLUSION: It was crucial to prevent HIV/AIDS and syphilis infections by improving the awareness of AIDS-related knowledge and changing related attitudes among the elderly. Further research aimed at identifying how these factors impact their sexual decision-making can shed valuable insight into further prevention program in this population.

Web-Based Risk Prediction Tool for an Individual's Risk of HIV and Sexually Transmitted Infections Using Machine Learning Algorithms: Development and External Validation Study.

BACKGROUND: HIV and sexually transmitted infections (STIs) are major global public health concerns. Over 1 million curable STIs occur every day among people aged 15 years to 49 years worldwide. Insufficient testing or screening substantially impedes the elimination of HIV and STI transmission. OBJECTIVE: The aim of our study was to develop an HIV and STI risk prediction tool using machine learning algorithms. METHODS: We used clinic consultations that tested for HIV and STIs at the Melbourne Sexual Health Centre between March 2, 2015, and December 31, 2018, as the development data set (training and testing data set). We also used 2 external validation data sets, including data from 2019 as external "validation data 1" and data from January 2020 and January 2021 as external "validation data 2." We developed 34 machine learning models to assess the risk of acquiring HIV, syphilis, gonorrhea, and chlamydia. We created an online tool to generate an individual's risk of HIV or an STI. RESULTS: The important predictors for HIV and STI risk were gender, age, men who reported having sex with men, number of casual sexual partners, and condom use. Our machine learning-based risk prediction tool, named MySTIRisk, performed at an acceptable or excellent level on testing data sets (area under the curve [AUC] for HIV=0.78; AUC for syphilis=0.84; AUC for gonorrhea=0.78; AUC for chlamydia=0.70) and had stable performance on both external validation data from 2019 (AUC for HIV=0.79; AUC for syphilis=0.85; AUC for gonorrhea=0.81; AUC for chlamydia=0.69) and data from 2020-2021 (AUC for HIV=0.71; AUC for syphilis=0.84; AUC for gonorrhea=0.79; AUC for chlamydia=0.69). CONCLUSIONS: Our web-based risk prediction tool could accurately predict the risk of HIV and STIs for clinic attendees using simple self-reported questions. MySTIRisk could serve as an HIV and STI screening tool on clinic websites or digital health platforms to encourage individuals at risk of HIV or an STI to be tested or start HIV pre-exposure prophylaxis. The public can use this tool to assess their risk and then decide if they would attend a clinic for testing. Clinicians or public health workers can use this tool to identify high-risk individuals for further interventions.

Fast one-pot synthesis of a Se-rich MnCdSe solid solution for highly efficient cocatalyst-free photocatalytic H2 evolution.

Designing high-efficiency and stable metal selenides for visible-light-induced photocatalytic H2 production has been challenging. Here, a novel class of Se-rich MnCdSe solid solution with a tunable band structure is fabricated through a fast one-pot strategy. In the absence of any cocatalysts, the optimal MnCdSe nanocrystals exhibit a much higher visible-light-driven H2 evolution activity (2582 µmol g-1 h-1) than the pristine CdSe (30 µmol g-1 h-1), and achieve an apparent quantum yield (AQY) of 7.5% at 420 nm. This work opens a new gateway to explore metal selenide-based solid solutions for photocatalytic applications.

Enhanced optical sensing performance in stacked resonant compound gratings.

We proposed a high-performance integrated optical sensor based on a stacked resonant compound grating (SRCG). The transmission spectrum of a SRCG is investigated by the theoretical model that combines the coupled mode theory with the eigenmode information of the grating structures. It is found that the spectral width of the SRCG is controlled by changing its structural parameters such as the strip depth, the period of the grating, and cavity length. The simulation results, which are verified by finite element method (FEM), show that the sensitivity of the sensor is 401.8 nm/RIU with its figure of merit (FOM) as high as 57404. The presented sensor is a promising application for high-performance biosensing.

Predicting the diagnosis of HIV and sexually transmitted infections among men who have sex with men using machine learning approaches.

OBJECTIVES: We aimed to develop machine learning models and evaluate their performance in predicting HIV and sexually transmitted infections (STIs) diagnosis based on a cohort of Australian men who have sex with men (MSM). METHODS: We collected clinical records of 21,273 Australian MSM during 2011-2017. We compared accuracies for predicting HIV and STIs (syphilis, gonorrhoea, chlamydia) diagnosis using four machine learning approaches against a multivariable logistic regression (MLR) model. RESULTS: Machine learning approaches consistently outperformed MLR. Gradient boosting machine (GBM) achieved the highest area under the receiver operator characteristic curve for HIV (76.3%) and STIs (syphilis, 85.8%; gonorrhoea, 75.5%; chlamydia, 68.0%), followed by extreme gradient boosting (71.1%, 82.2%, 70.3%, 66.4%), random forest (72.0%, 81.9%, 67.2%, 64.3%), deep learning (75.8%, 81.0%, 67.5%, 65.4%) and MLR (69.8%, 80.1%, 67.2%, 63.2%). GBM models demonstrated the ten greatest predictors collectively explained 62.7-73.6% of variations in predicting HIV/STIs. STIs symptoms, past syphilis infection, age, time living in Australia, frequency of condom use with casual male sexual partners during receptive anal sex and the number of casual male sexual partners in the past 12 months were most commonly identified predictors. CONCLUSIONS: Machine learning approaches are advantageous over multivariable logistic regression models in predicting HIV/STIs diagnosis.

Correction: Population genomic screening of all young adults in a health-care system: a cost-effectiveness analysis.

In the original version of this Article, the affiliation details for Lei Zhang were given as Monash University. While working on the Article Dr. Zhang was also affiliated with the Department of Epidemiology and Biostatistics, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, Shaanxi, PR China. This has now been corrected in both the PDF and HTML versions of the Article.

Population genomic screening of all young adults in a health-care system: a cost-effectiveness analysis.

PURPOSE: To consider the impact and cost-effectiveness of offering preventive population genomic screening to all young adults in a single-payer health-care system. METHODS: We modeled screening of 2,688,192 individuals, all adults aged 18-25 years in Australia, for pathogenic variants in BRCA1/BRCA2/MLH1/MSH2 genes, and carrier screening for cystic fibrosis (CF), spinal muscular atrophy (SMA), and fragile X syndrome (FXS), at 71% testing uptake using per-test costs ranging from AUD$200 to $1200 (~USD$140 to $850). Investment costs included genetic counseling, surveillance, and interventions (reimbursed only) for at-risk individuals/couples. Cost-effectiveness was defined below AUD$50,000/DALY (disability-adjusted life year) prevented, using an incremental cost-effectiveness ratio (ICER), compared with current targeted testing. Outcomes were cancer incidence/mortality, disease cases, and treatment costs reduced. RESULTS: Population screening would reduce variant-attributable cancers by 28.8%, cancer deaths by 31.2%, and CF/SMA/FXS cases by 24.8%, compared with targeted testing. Assuming AUD$400 per test, investment required would be between 4 and 5 times higher than current expenditure. However, screening would lead to substantial savings in medical costs and DALYs prevented, at a highly cost-effective ICER of AUD$4038/DALY. At AUD$200 per test, screening would approach cost-saving for the health system (ICER = AUD$22/DALY). CONCLUSION: Preventive genomic screening in early adulthood would be highly cost-effective in a single-payer health-care system, but ethical issues must be considered.

Phylogenetic and temporal dynamics of human immunodeficiency virus type 1 CRF01_AE and CRF07_BC among recently infected antiretroviral therapy-naïve men who have sex with men in Jiangsu province, China, 2012 to 2015: A molecular epidemiology-based study.

The prevalence and incidence of human immunodeficiency virus type 1 (HIV-1) among men who have sex with men (MSM) are on the rise throughout China. With a large population of MSM, Jiangsu Province is facing an escalating HIV-1 epidemic.The aim of this study was to explore the phylogenetic and temporal dynamics of HIV-1 CRF01_AE and CRF07_BC among antiretroviral therapy (ART)-naïve MSM recently infected with HIV-1 in Jiangsu Province.We recruited MSM in Jiangsu Province (Suzhou, Wuxi, Nantong, Taizhou and Yancheng) 2012 to 2015. We collected information on demographics and sexual behaviors and a blood sample for HIV genome RNA extraction, RT-PCR amplification, and DNA sequencing. Multiple alignments were made using Gene Cutter, with the selected reference sequences of various subtypes/recombinants from the Los Alamos HIV-1 database. Phylogenetic and Bayesian evolutionary analysis was performed by MEGA version 6.0, Fasttree v2.1.7. and BEAST v1.6.2. Categorical variables were analyzed using χ test (or Fisher exact test where necessary). χ test with trend was used to assess the evolution of HIV-1 subtype distribution over time. All data were analyzed using SPSS20.0 software package (IBM Company, New York, NY).HIV-1 phylogenetic analysis revealed a broad viral diversity including CRF01_AE (60.06%), CRF07_BC (22.29%), subtype B (5.88%), CRF67_01B (5.26%), CRF68_01B (2.79%), CRF55_01B (1.55%), CRF59_01B (0.93%), and CRF08_BC (0.62%). Two unique recombination forms (URFs) (0.62%) were also detected. Four epidemic clusters and 1 major cluster in CRF01_AE and CRF07_BC were identified. The introduction of CRF01_AE strain (2001) was earlier than CRF07_BC strain (2004) into MSM resided in Jiangsu based on the time of the most recent common ancestor.Our study demonstrated HIV-1 subtype diversity among ART-naïve MSM recently infected with HIV-1 in Jiangsu. We first depicted the spatiotemporal dynamics, traced the dates of origin for the HIV-1 CRF01_AE/07_BC strains and made inference for the effective population size among newly infected ART-naïve MSM in Jiangsu from 2012 to 2015. A real-time surveillance of HIV-1 viral diversity and phylodynamics of epidemic cluster would be of great value to the monitoring of the epidemic and control of transmission, improvement of antiretroviral therapy strategies, and design of vaccines.