In recent years, the rapid development of digital intelligence has provided a new path for rare disease data sharing and injected new power into the progress of research of rare diseases. This research is aimed at summarizing and consolidating relevant literatures on data sharing driven by digital intelligence (DI) in China and abroad, and constructing a local theoretical framework of DI-driven data sharing for rare diseases based on the status of rare diseases in China. Searching PubMed, EMbase, Cochrane, CNKI, Wanfang, and VIP database, we obtain a total of 214 representative literatures. Through literature review, we find that DI technologies have played important roles in different aspects of rare disease data sharing. China, the United States, and Europe have formed their own DI-driven data sharing systems for rare disease. From the theory of " Information Commons", we analyze the gap between China′s current situation and the goal of a " Rare Disease Data Commons". Based on the analysis, we put forward the idea of framework of " DI-STARS". China should develop the Data Sharing system making DI as the core of the system. Meanwhile, China should strengthen the data standardization system, create an innovation-encouraging environment, and build a bridge between different platforms. Using the DI-STARS theory, China will be able to build the " Rare Disease Data Commons" so that the diagnosis and treatment of rare diseases will be enhanced in China to meet the patients′ needs.

Objective To investigate the impact of sleep modes on the risk for diseases of the body systems.Methods Based on a subset of UK Biobank dataset comprising 87 617 participants,3 sleep dimensions including 6 sleep indicators were obtained through a wrist-worn accelerometer,that is sleep duration and onset,sleep rhythm(relative amplitude and stability),and sleep quality(sleep efficiency and number of awakenings).Latent profile analysis(LPA)was applied to identify and classify distinct sleep modes.Then their longitudinal medical records were the association between different sleep modes and the risk for 467 diseases.Results LPA identified 5 subgroups of unique sleep modes in the participants.Among the 5 subgroups,the subgroup 4 had relatively optimal levels in above sleep indicators.Compared to the subgroup 4,the other 4 subgroups exhibited variations in different sleep dimensions,with at least one indicator demonstrating an unfavorable trend.These subgroups also revealed differences in racial composition,shift work and social deprivation index.Moreover,there were notable differences in the risk of various system diseases among the subgroups(P<0.05).When compared to the subgroup 4,the other 4 subgroups exhibited an elevated risk for certain diseases(comprising a total of 126 diseases),with the diseases of the circulatory system,digestive system and musculoskeletal system most common.Among the 5 subgroups,the subgroup 2(shorter sleep duration and later sleep onset)and the subgroup 5(rhythm disorder)had the highest counts of associated risks,amounting to 85 and 91 types,respectively,but there was certain difference in their systematic composition.Conclusion There are different sleep modes within the participants,and the modes are potentially associated with an increased risk for diseases of body systems.Comprehensive interventions targeting overall sleep modes rather than single sleep indicator may yield obvious health benefits.

Objective:To describe the prevalence of daytime nap habit in participants of the China Kadoorie Biobank (CKB) study, across 10 study regions and explore its correlation with prevalence of major chronic diseases.Methods:Participants with a self-reported pre-diagnosis of any cancer at baseline survey were excluded. Logistic regression models were used to analyze the differences in study regions and age distribution of the prevalence daytime nap habit, and its correlation with the prevalence of diabetes, hypertension, coronary heart disease (CHD), stroke, chronic obstructive pulmonary disease (COPD), and chronic liver diseases.Results:Among 510 145 participants, 39.9% had daytime nap habit in summer and 20.8% had daytime nap habit all the year round. Urban-rural differences were observed in the prevalence of summer nap habit and perennial nap habit. Daytime nap in summer was common in rural areas and Suzhou, with prevalence ranged from 32.9% to 73.3%. Haikou and Liuzhou had higher prevalence of perennial nap (60.4% and 63.3%). The proportion of people with daytime nap habit all the year round increased with age ( P for trend <0.001), the proportion was highest in those aged 70- years (31.9%). Daytime nap habit in summer was positively correlated with the prevalence of diabetes, hypertension, CHD and chronic liver disease with OR of 1.10 (95% CI: 1.07-1.14), 1.03 (95% CI:1.02-1.05), 1.07 (95% CI: 1.02-1.12) and 1.07 (95% CI:1.00-1.14), respectively. Daytime nap habit all the year round was positively correlated with the prevalence of diabetes, hypertension, CHD, stroke, COPD and chronic liver disease with OR of 1.33 (95% CI: 1.29-1.37), 1.11 (95% CI: 1.09-1.13), 1.39 (95% CI: 1.33-1.45), 1.33 (95% CI: 1.26-1.41), 1.12 (95% CI: 1.08-1.16) and 1.27 (95% CI:1.18-1.37) respectively. Conclusion:There were regional and age differences in prevalence of daytime nap habit among CKB participants. Daytime nap habit, especially daytime nap habit all the year round, was positively correlated with the prevalence of major chronic diseases.

Objective:To evaluate the correlation of dietary patterns with low muscle mass, strength and quality in Chinese adults.Methods:Based on the second resurvey of China Kadoorie Biobank, factor analysis was conducted to derive dietary patterns from 20 food groups. Low muscle mass, strength, and quality were defined as the sex-specific lowest quintile of appendicular skeletal muscle mass index (ASMI), total skeletal muscle mass index (TSMI), handgrip strength and arm muscle quality (AMQ) according to Asian Working Group for Sarcopenia recommendations. Logistic regression models were used to evaluate the correlation of dietary patterns with low muscle mass, strength, and quality.Results:Two major dietary patterns were extracted. The balanced dietary pattern was characterized by the intake of a variety of foods, whereas the rice-meat dietary pattern was characterized by high intakes of rice, meat, poultry and fish. Individuals who had the highest quintile score of the balanced dietary pattern were less likely to have low TSMI, handgrip strength or AMQ( OR=0.83, 95% CI: 0.74-0.95 for low TSMI; OR=0.64, 95% CI: 0.56-0.74 for low handgrip strength; OR=0.82, 95% CI: 0.72-0.93 for low AMQ; for trend P<0.05). And those who scored higher on the rice-meat dietary pattern had lower risk of low muscle mass and strength ( OR=0.67, 95% CI: 0.55-0.82 for low ASMI; OR=0.69, 95% CI: 0.56-0.85 for low TSMI; OR=0.74, 95% CI: 0.60-0.91 for low handgrip strength; for trend P<0.05). Conclusion:Individuals followed the balanced dietary pattern, as well as those who followed the rice-meat dietary pattern, had better levels of skeletal muscle mass, strength and quality.

Objective:To explore the correlation between chronic diseases and muscle mass, strength and quality in adults in China.Methods:Based on the second resurvey of China Kadoorie Biobank (CKB) in 2013-2014, logistic regression models were used to analyze the correlation of different types, number and duration of chronic diseases with low muscle mass, handgrip strength and muscle quality.Results:The prevalence rate of diabetes, coronary heart disease (CHD), stroke and chronic obstructive pulmonary disease (COPD) were 9.6%, 5.8%, 3.2% and 26.8%, respectively, and 38.8% of the participants had at least one disease, and they were more likely to have low handgrip strength and low arm muscle quality (AMQ), and the longer the chronic diseases duration, the higher the risk. The ORs (95% CIs) for low handgrip strength and low AMQ in patients with 1 chronic disease for more than 10 years was 1.64 (1.42-1.90) and 1.83 (1.60-2.10), respectively. The ORs (95% CIs) for low handgrip strength were 1.26 (1.17-1.37), 1.42 (1.23-1.64) and 2.27 (1.55-3.32) and the ORs (95% CIs) for low AMQ were 1.28 (1.18-1.38), 1.67 (1.46-1.92) and 2.41(1.69-3.45), respectively, in patients with 1, 2, ≥3 chronic diseases, the correlation showed a linear trend ( P for trend <0.001). Diabetes, CHD and stroke were positively correlated with low handgrip strength and low AMQ. Compared with participants without COPD, COPD patients were more likely to have low appendicular skeletal muscle mass index (ASMI), low total skeletal muscle mass index (TSMI) and low handgrip strength, and the risk was positively correlated with disease duration. Conclusions:Patients with chronic diseases were more likely to have lower muscle strength and muscle quality, especially the patients with multi diseases and longer disease duration. The proportion of low handgrip strength and low AMQ was higher in patients who reported multi-prevalence and longer duration of chronic diseases.