Epidemiologic association and shared genetic architecture between cataract and hearing difficulties among middle-aged and older adults.

Age-related cataract and hearing difficulties are major sensory disorders that often co-exist in the global-wide elderly and have a tangible influence on the quality of life. However, the epidemiologic association between cataract and hearing difficulties remains unexplored, while little is known about whether the two share their genetic etiology. We first investigated the clinical association between cataract and hearing difficulties using the UK Biobank covering 502,543 individuals. Both unmatched analysis (adjusted for confounders) and a matched analysis (one control matched for each patient with cataract according to confounding factors) were undertaken and confirmed that cataract was associated with hearing difficulties (OR, 2.12; 95% CI, 1.98-2.27; OR, 2.03; 95% CI, 1.86-2.23, respectively). Furthermore, we explored and quantified the shared genetic architecture of these two complex sensory disorders at the common variant level using the bivariate causal mixture model (MiXeR) and conditional/conjunctional false discovery rate method based on the largest available genome-wide association studies of cataract (N = 585,243) and hearing difficulties (N = 323,978). Despite detecting only a negligible genetic correlation, we observe polygenic overlap between cataract and hearing difficulties and identify 6 shared loci with mixed directions of effects. Follow-up analysis of the shared loci implicates candidate genes QKI, STK17A, TYR, NSF, and TCF4 likely contribute to the pathophysiology of cataracts and hearing difficulties. In conclusion, this study demonstrates the presence of epidemiologic association between cataract and hearing difficulties and provides new insights into the shared genetic architecture of these two disorders at the common variant level.

EBD: an eye biomarker database.

MOTIVATION: Many ophthalmic disease biomarkers have been identified through comprehensive multiomics profiling, and hold significant potential in advancing the diagnosis, prognosis, and management of diseases. Meanwhile, the eye itself serves as a natural biomarker for several systemic diseases including neurological, renal, and cardiovascular systems. We aimed to collect and standardize this eye biomarkers information and construct the eye biomarker database (EBD) to provide ophthalmologists with a platform to search, analyze, and download these eye biomarker data. RESULTS: In this study, we present the EBD , a world-first online compilation comprising 889 biomarkers for 26 ocular diseases and 939 eye biomarkers for 181 systemic diseases. The EBD also includes the information of 78 "nonbiomarkers"-the objects that have been proven cannot be biomarkers. Biological function and network analysis were conducted for these ocular disease biomarkers, and several hub pathways and common network topology characteristics were newly identified, which may promote future ocular disease biomarker discovery and characterizes the landscape of biomarkers for eye diseases at the pathway and network level. The EBD is expected to yield broader utility among developmental biologists and clinical scientists in and outside of the eye field by assisting in the identification of biomarkers linked to eye disorders and related systemic diseases. AVAILABILITY AND IMPLEMENTATION: EBD is available at http://www.eyeseeworld.com/ebd/index.html.

Plasma metabolomics identifies key metabolites and improves prediction of diabetic retinopathy: development and validation across multi-national cohorts.

PURPOSE: To identify longitudinal metabolomic fingerprints of diabetic retinopathy (DR) and evaluate their utility in predicting DR development and progression. DESIGN: Multicenter, multi-ethnic cohort study. PARTICIPANTS: This study included 17,675 participants with baseline pre-diabetes/diabetes, in accordance with the 2021 American Diabetes Association guideline, and free of baseline DR from the UK Biobank (UKB); and an additional 638 diabetic participants from the Guangzhou Diabetic Eye Study (GDES) for external validation. METHODS: Longitudinal DR metabolomic fingerprints were identified through nuclear magnetic resonance assay in UKB participants. The predictive value of these fingerprints for predicting DR development were assessed in a fully withheld test set. External validation and extrapolation analyses of DR progression and microvascular damage were conducted in the GDES cohort. Model assessments included the C-statistic, net classification improvement (NRI), integrated discrimination improvement (IDI), calibration, and clinical utility in both cohorts. MAIN OUTCOME MEASURES: DR development, progression, and retinal microvascular damage. RESULTS: Of 168 metabolites, 118 were identified as candidate metabolomic fingerprints for future DR development. These fingerprints significantly improved the predictability for DR development beyond traditional indicators (C-statistic: 0.802, 95% CI, 0.760-0.843 vs. 0.751, 95% CI, 0.706-0.796; P = 5.56×10-4). Glucose, lactate, and citrate were among the fingerprints validated in the GDES cohort. Using these parsimonious and replicable fingerprints yielded similar improvements for predicting DR development (C-statistic: 0.807, 95% CI, 0.711-0.903 vs. 0.617, 95% CI, 0.494, 0.740; P = 1.68×10-4) and progression (C-statistic: 0.797, 95% CI, 0.712-0.882 vs. 0.665, 95% CI, 0.545-0.784; P = 0.003) in the external cohort. Improvements in NRIs, IDIs, and clinical utility were also evident in both cohorts (all P <0.05). In addition, lactate and citrate were associated to microvascular damage across macular and optic disc regions (all P <0.05). CONCLUSIONS: Metabolomic profiling has proven effective in identifying robust fingerprints for predicting future DR development and progression, providing novel insights into the early and advanced stages of DR pathophysiology.

Leading determinants of incident dementia among individuals with and without the apolipoprotein E ε4 genotype: a retrospective cohort study.

BACKGROUND: Little is known regarding the leading risk factors for dementia/Alzheimer's disease (AD) in individuals with and without APOE4. The identification of key risk factors for dementia/Alzheimer's disease (AD) in individuals with and without the APOE4 gene is of significant importance in global health. METHODS: Our analysis included 110,354 APOE4 carriers and 220,708 age- and sex-matched controls aged 40-73 years at baseline (between 2006-2010) from UK Biobank. Incident dementia was ascertained using hospital inpatient, or death records until January 2021. Individuals of non-European ancestry were excluded. Furthermore, individuals without medical record linkage were excluded from the analysis. Moderation analysis was tested for 134 individual factors. RESULTS: During a median follow-up of 11.9 years, 4,764 cases of incident all-cause dementia and 2065 incident AD cases were documented. Hazard ratios (95% CIs) for all-cause dementia and AD associated with APOE4 were 2.70(2.55-2.85) and 3.72(3.40-4.07), respectively. In APOE4 carriers, the leading risk factors for all-cause dementia included low self-rated overall health, low household income, high multimorbidity risk score, long-term illness, high neutrophil percentage, and high nitrogen dioxide air pollution. In non-APOE4 carriers, the leading risk factors included high multimorbidity risk score, low overall self-rated health, low household income, long-term illness, high microalbumin in urine, high neutrophil count, and low greenspace percentage. Population attributable risk for these individual risk factors combined was 65.1%, and 85.8% in APOE4 and non-APOE4 carriers, respectively. For 20 risk factors including multimorbidity risk score, unhealthy lifestyle habits, and particulate matter air pollutants, their associations with incident dementia were stronger in non-APOE4 carriers. For only 2 risk factors (mother's history of dementia, low C-reactive protein), their associations with incident all-cause dementia were stronger in APOE4 carriers. CONCLUSIONS: Our findings provide evidence for personalized preventative approaches to dementia/AD in APOE4 and non-APOE4 carriers. A mother's history of dementia and low levels of C-reactive protein were more important risk factors of dementia in APOE4 carriers whereas leading risk factors including unhealthy lifestyle habits, multimorbidity risk score, inflammation and immune-related markers were more predictive of dementia in non-APOE4 carriers.

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.

Central obesity and its association with retinal age gap: insights from the UK Biobank study.

BACKGROUND: Conflicting evidence exists on the association between ageing and obesity. Retinal age derived from fundus images has been validated as a novel biomarker of ageing. In this study, we aim to investigate the association between different anthropometric phenotypes based on body mass index (BMI) and waist circumference (WC) and the retinal age gap (retinal age minus chronological age). METHODS: A total of 35,550 participants with BMI, WC and qualified retinal imaging data available were included to investigate the association between anthropometric groups and retinal ageing. Participants were stratified into 7 different body composition groups based on BMI and WC (Normal-weight/Normal WC, Overweight/Normal WC, Mild obesity/Normal WC, Normal-weight/High WC, Overweight/High WC, Mild obesity/High WC, and Severe obesity/High WC). Linear regression and logistic regression models were fitted to investigate the association between the seven anthropometric groups and retinal age gap as continuous and categorical outcomes, respectively. RESULTS: A total of 35,550 participants (55.6% females) with a mean age 56.8 ± 8.04 years were included in the study. Individuals in the Overweight/High WC, Mild obesity/High WC and Severe obesity/High WC groups were associated with an increase in the retinal age gap, compared with those in the Normal Weight/Normal WC group (ß = 0.264, 95% CI: 0.105-0.424, P =0.001; ß = 0.226, 95% CI: 0.082-0.371, P = 0.002; ß = 0.273, 95% CI: 0.081-0.465, P = 0.005; respectively) in fully adjusted models. Similar findings were noted in the association between the anthropometric groups and retinal ageing process as a categorical outcome. CONCLUSION: A significant positive association exists between central obesity and accelerated ageing indexed by retinal age gaps, highlighting the significance of maintaining a healthy body shape.

Association of type 1 diabetes and age at diagnosis of type 2 diabetes with brain volume and risk of dementia in the UK Biobank: A prospective cohort study of community-dwelling participants.

AIMS: To investigate the association of type 1 diabetes (T1D) and age at diagnosis of type 2 diabetes (T2D) with brain structure and incident dementia. METHODS: Our analysis was based on the UK Biobank. We included 1376 participants with diabetes and 2752 randomly selected controls for brain volume analysis, and 25,141 participants with diabetes and 50,282 randomly selected controls for dementia analysis. Brain volume was measured using magnetic resonance imaging. Dementia was identified using hospital inpatient records and mortality register data until January 2021. RESULTS: T2D diagnosed at a younger age was associated with larger reductions in brain volume. After adjustment for glycated haemoglobin (HbA1c) and other covariates, only T2D diagnosed <50 years was associated with smaller total brain volume (ß (95% CI): -14.56 (-24.67, -4.44) ml), and grey (-6.47[-12.75, -0.20] ml) and white matter volumes (-8.08[-14.66, -1.51] ml). Corresponding numbers for total brain, grey matter and white matter volumes associated with T1D were -62.86 (-93.71,-32.01), -34.27 (-53.72, -14.83), and -28.59 (-47.65, -9.52) ml, respectively. During a median follow-up of 11.9 years, 2035 new dementia cases were identified. Younger age at diagnosis of T2D was associated with larger excessive risk of dementia, whereas T2D diagnosed <50 years was associated with the largest hazard ratio (HR) (95% CI: 2.03[1.53-2.69]) in the multivariable analysis. The HR (95% CI) for dementia associated with T1D was 2.08 (1.40-3.09). CONCLUSION: Individuals with T1D or T2D diagnosed at younger age are at larger excessive risk of brain volume reduction and dementia.

Association of Retinal Age Gap With Arterial Stiffness and Incident Cardiovascular Disease.

BACKGROUND: Retinal parameters could reflect systemic vascular changes. With the advances of deep learning technology, we have recently developed an algorithm to predict retinal age based on fundus images, which could be a novel biomarker for aging and mortality. Therefore, we aim to investigate associations of retinal age gap with arterial stiffness index and incident cardiovascular disease (CVD). METHODS: A deep learning model was trained based on 19 200 fundus images of 11 052 participants without any medical history at baseline to predict the retinal age. Retinal age gap (retinal age predicted minus chronological age) was generated for the remaining 35 917 participants. Regression models were used to assess the association between retinal age gap and arterial stiffness index. Cox proportional hazards regression models and restricted cubic splines were used to explore the association between retinal age gap and incident CVD. RESULTS: We found each 1-year increase in retinal age gap was associated with increased arterial stiffness index (ß=0.002 [95% CI, 0.001-0.003]; P<0.001). After a median follow-up of 5.83 years (interquartile range: 5.73-5.97), 675 (2.00%) developed CVD. In the fully adjusted model, each 1-year increase in retinal age gap was associated with a 3% increase in the risk of incident CVD (hazard ratio=1.03 [95% CI, 1.01-1.06]; P=0.014). In the restricted cubic splines analysis, the risk of incident CVD increased significantly when retinal age gap reached 1.21 (hazard ratio=1.05 [95% CI, 1.00-1.10]; P-overall <0.0001; P-nonlinear=0.0681). CONCLUSIONS: We found that retinal age gap was significantly associated with arterial stiffness index and incident CVD events, supporting the potential of this novel biomarker in identifying individuals at high risk of future CVD events.

Temporal trajectories of important diseases in the life course and premature mortality in the UK Biobank.

BACKGROUND: Little is known regarding life-course trajectories of important diseases. We aimed to identify diseases that were strongly associated with mortality and test temporal trajectories of these diseases before mortality. METHODS: Our analysis was based on UK Biobank. Diseases were identified using questionnaires, nurses' interviews, or inpatient data. Mortality register data were used to identify mortality up to January 2021. The association between 60 individual diseases at baseline and in the life course and incident mortality was examined using Cox proportional regression models. Those diseases with great contribution to mortality were identified and disease trajectories in life course were then derived. RESULTS: During a median follow-up of 11.8 years, 31,373 individuals (median age at death (interquartile range): 70.7 (65.3-74.8) years, 59.4% male) died of all-cause mortality (with complete data on diagnosis date of disease), with 16,237 dying with cancer and 6702 with cardiovascular disease (CVD). We identified 37 diseases including cancers and heart diseases that were associated with an increased risk of mortality independent of other diseases (hazard ratio ranged from 1.09 to 7.77). Among those who died during follow-up, 2.2% did not have a diagnosis of any disease of interest and 90.1% were diagnosed with two or more diseases in their life course. Individuals who were diagnosed with more diseases in their life course were more likely to have longer longevity. Cancer was more likely to be diagnosed following hypertension, hypercholesterolemia, CVD, or digestive disorders and more likely to be diagnosed ahead of CVD, chronic kidney disease (CKD), or digestive disorders. CVD was more likely to be diagnosed following hypertension, hypercholesterolemia, or digestive disorders and more likely to be diagnosed ahead of cancer or CKD. Hypertension was more likely to precede other diseases, and CKD was more likely to be diagnosed as the last disease before more mortality. CONCLUSIONS: There are significant interplays between cancer and CVD for mortality. Cancer and CVD were frequently clustered with hypertension, CKD, and digestive disorders with CKD highly being diagnosed as the last disease in the life course. Our findings underline the importance of health checks among middle-aged adults for the prevention of premature mortality.

Retinal age gap as a predictive biomarker of stroke risk.

BACKGROUND: The aim of this study is to investigate the association of retinal age gap with the risk of incident stroke and its predictive value for incident stroke. METHODS: A total of 80,169 fundus images from 46,969 participants in the UK Biobank cohort met the image quality standard. A deep learning model was constructed based on 19,200 fundus images of 11,052 disease-free participants at baseline for age prediction. Retinal age gap (retinal age predicted based on the fundus image minus chronological age) was generated for the remaining 35,917 participants. Stroke events were determined by data linkage to hospital records on admissions and diagnoses, and national death registers, whichever occurred earliest. Cox proportional hazards regression models were used to estimate the effect of retinal age gap on risk of stroke. Logistic regression models were used to estimate the predictive value of retinal age and well-established risk factors in 10-year stroke risk. RESULTS: A total of 35,304 participants without history of stroke at baseline were included. During a median follow-up of 5.83 years, 282 (0.80%) participants had stroke events. In the fully adjusted model, each one-year increase in the retinal age gap was associated with a 4% increase in the risk of stroke (hazard ratio [HR] = 1.04, 95% confidence interval [CI]: 1.00-1.08, P = 0.029). Compared to participants with retinal age gap in the first quintile, participants with retinal age gap in the fifth quintile had significantly higher risks of stroke events (HR = 2.37, 95% CI: 1.37-4.10, P = 0.002). The predictive capability of retinal age alone was comparable to the well-established risk factor-based model (AUC=0.676 vs AUC=0.661, p=0.511). CONCLUSIONS: We found that retinal age gap was significantly associated with incident stroke, implying the potential of retinal age gap as a predictive biomarker of stroke risk.

Plasma metabolomic profiles of dementia: a prospective study of 110,655 participants in the UK Biobank.

BACKGROUND: Plasma metabolomic profile is disturbed in dementia patients, but previous studies have discordant conclusions. METHODS: Circulating metabolomic data of 110,655 people in the UK Biobank study were measured with nuclear magnetic resonance technique, and incident dementia records were obtained from national health registers. The associations between plasma metabolites and dementia were estimated using Cox proportional hazard models. The 10-fold cross-validation elastic net regression models selected metabolites that predicted incident dementia, and a 10-year prediction model for dementia was constructed by multivariable logistic regression. The predictive values of the conventional risk model, the metabolites model, and the combined model were discriminated by comparison of area under the receiver operating characteristic curves (AUCs). Net reclassification improvement (NRI) was used to estimate the change of reclassification ability when adding metabolites into the conventional prediction model. RESULTS: Amongst 110,655 participants, the mean (standard deviation) age was 56.5 (8.1) years, and 51 186 (46.3%) were male. A total of 1439 (13.0%) developed dementia during a median follow-up of 12.2 years (interquartile range: 11.5-12.9 years). A total of 38 metabolites, including lipids and lipoproteins, ketone bodies, glycolysis-related metabolites, and amino acids, were found to be significantly associated with incident dementia. Adding selected metabolites (n=24) to the conventional dementia risk prediction model significantly improved the prediction for incident dementia (AUC: 0.824 versus 0.817, p =0.042) and reclassification ability (NRI = 4.97%, P = 0.009) for identifying high risk groups. CONCLUSIONS: Our analysis identified various metabolomic biomarkers which were significantly associated with incident dementia. Metabolomic profiles also provided opportunities for dementia risk reclassification. These findings may help explain the biological mechanisms underlying dementia and improve dementia prediction.

Retinal age gap as a predictive biomarker of future risk of Parkinson's disease.

INTRODUCTION: retinal age derived from fundus images using deep learning has been verified as a novel biomarker of ageing. We aim to investigate the association between retinal age gap (retinal age-chronological age) and incident Parkinson's disease (PD). METHODS: a deep learning (DL) model trained on 19,200 fundus images of 11,052 chronic disease-free participants was used to predict retinal age. Retinal age gap was generated by the trained DL model for the remaining 35,834 participants free of PD at the baseline assessment. Cox proportional hazards regression models were utilised to investigate the association between retinal age gap and incident PD. Multivariable logistic model was applied for prediction of 5-year PD risk and area under the receiver operator characteristic curves (AUC) was used to estimate the predictive value. RESULTS: a total of 35,834 participants (56.7 ± 8.04 years, 55.7% female) free of PD at baseline were included in the present analysis. After adjustment of confounding factors, 1-year increase in retinal age gap was associated with a 10% increase in risk of PD (hazard ratio [HR] = 1.10, 95% confidence interval [CI]: 1.01-1.20, P = 0.023). Compared with the lowest quartile of the retinal age gap, the risk of PD was significantly increased in the third and fourth quartiles (HR = 2.66, 95% CI: 1.13-6.22, P = 0.024; HR = 4.86, 95% CI: 1.59-14.8, P = 0.005, respectively). The predictive value of retinal age and established risk factors for 5-year PD risk were comparable (AUC = 0.708 and 0.717, P = 0.821). CONCLUSION: retinal age gap demonstrated a potential for identifying individuals at a high risk of developing future PD.

PROGRESSIVE PERIPAPILLARY CHOROID THINNING AND RETINAL NEURODEGENERATION IN PATIENTS WITH DIABETES: A 3-Year Cohort Study.

PURPOSE: To investigate longitudinal changes in peripapillary choroidal thickness (pCT) and retinal nerve fiber thickness (pRNFLT) in patients with Type 2 diabetes mellitus. METHODS: This was a prospective observational cohort study. Patients with Type 2 diabetes mellitus without diabetic retinopathy (DR) at baseline were recruited, followed up for three years, and further divided into an incident DR group and a non-DR group according to the outcome. The pCT and pRNFLT were measured through swept-source optical coherence tomography at 1-year interval, and the mean rates of pCT and pRNFLT thinning were compared between the DR groups. RESULTS: A total of 682 patients (682 eyes) were included in the final analysis. After 3-years follow-up, 122 (17.89%) developed DR. Both pCT and pRNFLT progressively thinned (-2.37 [-2.80 to -1.95] µm/year; -0.40 [-0.55 to -0.25] µm/year, respectively, P < 0.05) and accelerated thinning was observed in the incident DR group. The rates of pCT thinning (-3.92 [-4.96 to -2.88] µm/year, -2.03 [-2.49 to -1.57] µm/year, respectively) and pRNFLT loss (-1.03 [-1.31 to -0.76] µm/year, -0.26 [-0.43 to -0.09] µm/year, respectively) in the incident DR group were 1.93 and 3.96 times faster than those in the non-DR group, respectively. In addition, pCT and pRNFLT thinning were negatively related in Type 2 diabetes mellitus population, and faster pCT thinning indicated slower pRNFLT loss. CONCLUSION: Patients with Type 2 diabetes mellitus were at a higher risk of developing DR when accelerated pCT and pRNFLT thinning were present, indicating that heavier choroidal damage and retinal neurodegeneration precede clinical DR. The pCT and pRNFLT have the potential to serve as novel sensitive biomarkers of preclinical and early DR.

Association between dual sensory impairment and risk of mortality: a cohort study from the UK Biobank.

BACKGROUND: Dual sensory impairment is affecting over 10% of older adults worldwide. However, the long-term effect of dual sensory impairment (DSI) on the risk of mortality remains controversial. We aim to investigate the impact of single or/and dual sensory impairment on the risk of mortality in a large population-based sample of the adult in the UK with 14-years of follow-up. METHODS: This population-based prospective cohort study included participants aged 40 and over with complete records of visual and hearing functions from the UK Biobank study. Measurements of visual and hearing functions were performed at baseline examinations between 2006 and 2010, and data on mortality was obtained by 2021. Dual sensory impairment was defined as concurrent visual and hearing impairments. Cox proportional hazards regression models were employed to evaluate the impact of sensory impairment (dual sensory impairment, single visual or hearing impairment) on the hazard of mortality. RESULTS: Of the 113,563 participants included in this study, the mean age (standard deviation) was 56.8 (8.09) years, and 61,849 (54.5%) were female. At baseline measurements, there were 733 (0.65%) participants with dual sensory impairment, 2,973 (2.62%) participants with single visual impairment, and 13,560 (11.94%) with single hearing impairment. After a follow-up period of 14 years (mean duration of 11 years), 5,992 (5.28%) participants died from all causes. Compared with no sensory impairment, dual sensory impairment was significantly associated with an estimated 44% higher hazard of mortality (hazard ratio: 1.44 [95% confidence interval, 1.11-1.88], p = 0.007) after multiple adjustments. CONCLUSIONS: Individuals with dual sensory impairment were found to have an independently 44% higher hazard of mortality than those with neither sensory impairment. Timely intervention of sensory impairment and early prevention of its underlying causes should help to reduce the associated risk of mortality.

Use of antihypertensive medications and the risk of glaucoma onset: Findings from the 45 and Up Study.

BACKGROUND: Abnormal blood pressure is a potential risk factor for glaucoma. However, the role of antihypertensive medications on glaucoma pathogenesis is controversial. This study aims to investigate the association between the use of antihypertensive medications and glaucoma onset. METHODS: This nested case-control study, based on a large-scale longitudinal cohort in Australia, retrieved participants' claims records on drugs and Medicare services from national health databases. Participants with three or more claim records of anti-glaucoma medications from 2009 to 2016 were classified as glaucoma patients; those with none were classified as controls. Claim records of antihypertensive medications were identified within the 5 years before glaucoma onset and contemporary periods in matched controls without glaucoma. The association between the use of antihypertensive medications and glaucoma onset was assessed by multivariable logistic regression models. RESULTS: A total of 6748 cases and 13 496 controls were analysed. Compared with controls, the proportion of users of antihypertensive medications was slightly higher in glaucoma patients (46.9% vs. 46.0%, p > 0.05). After adjustments for demographics, health-related factors and medical history, the association between the use of antihypertensive medications and glaucoma onset was nonsignificant (OR 0.95, 95% CI = 0.89-1.02). As for specific subtypes, only beta-blocking agents (BBA) (OR 0.82, 95% CI = 0.75-0.90) and diuretics (OR 0.85, 95% CI = 0.77-0.95) were significantly associated with reduced risks of glaucoma onset. CONCLUSIONS: This study indicated that the use of antihypertensive medications was not associated with glaucoma onset. Decreased risks of glaucoma onset in users of BBA and diuretics require further validation.

Speed of Movement, Fatness, and the Change in Cardiometabolic Risk Factors in Children.

We aimed to examine speed of movement and its interactive association with fatness to changes in cardiometabolic risk factors over one year in children. The analysis included 8345 children aged 6-13 years. Cardiometabolic risk score was computed by summing Z-scores of waist circumference, the average of systolic and diastolic blood pressure, fasting glucose, high-density lipoprotein cholesterol (multiplied by -1), and triglycerides. Both high baseline and improvement in speed of movement were associated with favourable changes in percent body fat, lipids, and cardiometabolic risk score. Percentages of the association between baseline speed of movement and changes in cardiometabolic risk score, triglycerides, and high-density lipoprotein cholesterol explained by baseline BMI were 24.6% (19.6-29.1%), 26.2% (19.7-31.1%), and 12.5% (9.6-15.4%), respectively. The corresponding number for percent body fat was 47.0% (40.4-54.1%), 43.3% (36.7-51.7%), and 29.8% (25.0-34.6%), respectively. Speed of movement mediated the association between fatness and cardiometabolic risk factors. Improved speed of movement was associated with a lower increase in blood pressure in obese children only. Speed of movement is a strong predictor of changes in cardiometabolic risk factors. Fatness and speed of movement are interactively associated with cardiometabolic risk factors. Speed of movement may attenuate the positive association between fatness and blood pressure.

The Association between Vision Impairment and Incidence of Dementia and Cognitive Impairment: A Systematic Review and Meta-analysis.

TOPIC: The magnitude and direction of the association between vision impairment and incident dementia and cognitive impairment in prospective cohort studies was estimated by systematic review and meta-analysis. The global burden of dementia associated with vision impairment then was estimated. CLINICAL RELEVANCE: Because a predominant proportion of vision impairment is preventable or treatable, investigating its association with dementia may help to identify an important modifiable factor for the prevention of dementia. METHODS: A literature search was conducted using PubMed, Embase, Web of Science, and Google Scholar on September 15, 2020. Relative risks (RRs) were pooled using random-effects models and stratified analyses for subgroups representing different study characteristics. Publication bias was evaluated with funnel plots and the Egger test. The global burden of dementia associated with vision impairment was estimated based on the Global Burden of Disease Study data on the prevalence of dementia and vision impairment. RESULTS: In the meta-analysis of 14 prospective cohort studies with 6 204 827 participants and 171 888 dementia patients, the pooled RR associated with vision impairment was 1.47 (95% confidence interval [CI], 1.36-1.60). In the meta-analysis of 12 prospective cohort studies with 45 313 participants and 13 350 patients with cognitive impairment, the pooled RR was 1.35 (95% CI, 1.28-1.41). Stratified analyses showed that the associations of vision impairment with incident dementia and cognitive impairment were similar across methods of vision assessment, length of follow-up, and study quality. The global number of people with dementia associated with moderate or severe vision impairment in 2016 was 2.1 million (80% uncertainty interval, 1.0-3.3 million), which accounted for 4.7% (95% CI, 2.3%-7.5%) of the global burden of dementia. Economic inequality was significant for the burden of dementia associated with vision impairment. DISCUSSION: The overall quality of the body evidence was low because of the observational design of the studies included in the analysis. Vision impairment is associated with an increased risk of both dementia and cognitive impairment in older adults. Screening and treating vision impairment, especially in low- and middle-income countries, may help to alleviate the global burden of dementia.

Leading determinants for multimorbidity in middle-aged Australian men and women: A nine-year follow-up cohort study.

Although socioeconomic, behavioural, psychological, and biological factors have been individually linked to multimorbidity, data on the importance of these factors are limited. Our study aimed to determine the leading predictors for multimorbidity of chronic conditions in middle-aged Australian adults using machine learning methods. We included 53,867 participants aged 45-64 years from the 45 and Up Study who were free of eleven predefined chronic conditions at baseline (2006-2009) in the analysis. Incident multimorbidity was defined by the co-existence of ≥2, ≥3, or ≥ 4 conditions during follow-up until December 31, 2016. The five leading predictors for multimorbidity in men were age (7.2-20.5% of total variance), body mass index (6.5-15.4%), smoking (4.0-8.3%), chicken intake (3.6-7.5%), and red meat intake (4.6-6.3%) across the three definitions. Leading predictors varied across the three definitions in women, but the four common ones were body mass index (6.3-20.1%), age (6.2-16.4%), chicken intake (4.1-8.3%), and red meat intake (4.2-4.7%). The ten leading modifiable health factors accounted for 39.4-46.1% of total variance across the three definitions. Men with 6-10 health factors had 46-54% lower risks for multimorbidity compared with those reporting ≤2. The corresponding percentage for women was 45-52%. Non-behavioural factors including psychological distress, low education and income and high relative economic disadvantage were among the leading risk factors for multimorbidity. In conclusion, modifications on behavioural factors including diets, physical activity, smoking, alcohol consumption may reduce the risk of multimorbidity in middle-aged adults, whereas individuals with low socioeconomic status or psychological distress are at the highest priority for intervention.

Effect of multidimensional lifestyle interventions on metabolic risk reduction in children: a cluster randomised controlled trial.

Few clinical trials have investigated lifestyle intervention effect on metabolic health in children. The study aimed to examine the effect of diet and physical activity intervention on the reduction of clustered metabolic risk score (CMRS) in children and moderators and mediators of the intervention effect. A multicentre, clustered randomised controlled trial was conducted with examination conducted at baseline and after intervention over one year. 7110 children (49.7% girls) with a mean of 9.06 (95% CI: 9.03, 9.09) years were included in the analysis. In Beijing, each three schools were randomly assigned to diet-only, physical activity-only intervention and control groups. In five other urban cities, each 15 schools were randomly assigned to comprehensive intervention and control groups. CMRS was computed by summing the Z scores of % fat mass, systolic blood pressure, fasting glucose, ratio of cholesterol to high-density lipoprotein, and triglyceride. Compared with controls (n = 2808), children in the comprehensive intervention group (n = 2848) had more reduction in CMRS (multivariate-adjusted mean difference (95% CI): -0.49 (-0.85, -0.14)). The body mass index (BMI) reduction explained 7.3% (95% CI 2.8%-18.1%) of the total intervention effect. The intervention was more effective in children with higher birthweight, lower parental BMI, or complete parental data. Diet-only or physical activity-only intervention had non-significant effects on CMRS reduction. Our multidimensional comprehensive intervention resulted in significant reduction in CMRS in primary school children and this effect was modified by birthweight, parental BMI, and parental involvement. A minority of metabolic risk reduction was mediated through BMI. Clinical Trial Registry number and website: ChiCTR-PRC-09000402, URL: http://www.chictr.org.cn.

Leading dietary determinants identified using machine learning techniques and a healthy diet score for changes in cardiometabolic risk factors in children: a longitudinal analysis.

BACKGROUND: Identifying leading dietary determinants for cardiometabolic risk (CMR) factors is urgent for prioritizing interventions in children. We aimed to identify leading dietary determinants for the change in CMR and create a healthy diet score (HDS) to predict CMR in children. METHODS: We included 5676 children aged 6-13 years in the final analysis with physical examinations, blood tests, and diets assessed at baseline and one year later. CMR score (CMRS) was computed by summing Z-scores of waist circumference, an average of systolic and diastolic blood pressure (SBP and DBP), fasting glucose, high-density lipoprotein cholesterol (HDL-C, multiplying by - 1), and triglycerides. Machine learning was used to identify leading dietary determinants for CMR and an HDS was then computed. RESULTS: The nine leading predictors for CMRS were refined grains, seafood, fried foods, sugar-sweetened beverages, wheat, red meat other than pork, rice, fungi and algae, and roots and tubers with the contribution ranging from 3.9 to 19.6% of the total variance. Diets high in seafood, rice, and red meat other than pork but low in other six food groups were associated with a favorable change in CMRS. The HDS was computed based on these nine dietary factors. Children with HDS ≥8 had a higher decrease in CMRS (ß (95% CI): - 1.02 (- 1.31, - 0.73)), BMI (- 0.08 (- 0.16, - 0.00)), SBP (- 0.46 (- 0.58, - 0.34)), DBP (- 0.46 (- 0.58, - 0.34)), mean arterial pressure (- 0.50 (- 0.62, - 0.38)), fasting glucose (- 0.22 (- 0.32, - 0.11)), insulin (- 0.52 (- 0.71, - 0.32)), and HOMA-IR (- 0.55 (- 0.73, - 0.36)) compared to those with HDS ≦3. Improved HDS during follow-up was associated with favorable changes in CMRS, BMI, percent body fat, SBP, DBP, mean arterial pressure, HDL-C, fasting glucose, insulin, and HOMA-IR. CONCLUSION: Diets high in seafood, rice, and red meat other than pork and low in refined grains, fried foods, sugar-sweetened beverages, and wheat are leading healthy dietary factors for metabolic health in children. HDS is strongly predictive of CMR factors.