Introduction: The aim of the study was to examine the association between frailty and osteoarthritis.Material and methods: We conducted a cross-sectional study in the National Health and Nutrition Examination Survey, while logistic regression was used to explore the association of the two. Mendelian randomization (MR) study was used to explore the causal relationship between the two.Results: In the cross-sectional study, logistic regression analysis showed that odds ratio (OR) (95% CI) value was 1.07 (1.05, 1.08). In the MR study, the inverse-variance weighting (IVW) results showed OR (95% CI) value of 1.69 (1.01–2.83).Conclusions: There is both a correlation and a causal relationship between frailty and osteoarthritis, and frailty may be a potentially better response than age to osteoarthritis.

Summary: Objective: To describe the epidemiological and clinical characteristics of the Coronavirus Disease 2019 (COVID-19) hospitalized patients and to offer suggestions to the urgent needs of COVID-19 prevention, diagnosis and treatment. Methods: We included 102 confirmed COVID-19 cases hospitalized in Xiangyang No.1 people’s hospital, Hubei, China until Feb 9th, 2020. Demographic data, laboratory findings and chest computed tomographic (CT) images were obtained and analyzed. Findings: All cases were confirmed by real-time RT-PCR, including 52 males and 50 females with a mean age of 50.38 years (SD 16.86). Incubation time ranged from one to twenty days with a mean period of 8.09 days (SD 4.99). Fever (86[84.3%] of 102 patients), cough (58[57%]), fatigue (28[27%]), shortness of breath (24[23%]), diarrhea (15[15%]), expectoration (13[12%]), inappetence (11[10%]) were common clinical manifestations. We observed a decreased blood leukocyte count and lymphopenia in 21 (20.6%) and 56 (54.9%) patients, respectively. There were 66 (68%) of 97 patients with elevated C-reactive protein levels and 49 (57.6%) of 85 with increased erythrocytes sedimentation rate. Higher levels of procalcitonin and ferritin were observed in 19 (25.3%) of 75 and 12 (92.3%) of 13 patients, respectively. Eight patients were admitted to intensive care unit (ICU), six developed respiratory failure, three had multiple organ failure and three died. The cumulative positivity rate over three rounds of real-time RT-PCR was 96%. One-hundred patients were found with typical radiological abnormalities in two rounds of chest CT scans, indicating a 98% consistency with real-time RT-PCR results. Interpretation: Most COVID-19 patients in Xiangyang were secondary cases without sex difference, and the rate of severe case and death was low. Middle-to-old-age individuals were more susceptible to the virus infection and the subsequent development of severe/fatal consequences. The average incubation period was longer among our patients. We recommend prolonging the quarantine period to three weeks. Three times real-time RT-PCR plus two times CT scans is a practical clinical diagnosis strategy at present and should be used to increase the accuracy of diagnosis, thereby controlling the source of infection more effectively.

Added sugars intake is common among individuals with Parkinson’s disease (PD), yet the link between added sugars intake and PD is not well understood. Our study aims to investigate the association between added sugars intake and PD. This study utilized data from the National Health and Nutrition Examination Survey (NHANES) from1990 to 2020. Added sugars intake was estimated based on a 24-hour dietary recall from participants. Multivariable logistic regression analysis was employed to explore the relationship between added sugars intake and the prevalence of PD. Restricted cubic spline (RCS) was used to explore the nonlinear association between added sugars intake and PD. To further observe whether the conclusions were consistent across different subgroups, we conducted subgroup analyses to investigate the association of added sugars intake with PD in different populations. The study included 12,489 participants, of which 100 had PD. When weighted, the data represented 136,959,144 participants. The study revealed a positive association between added sugars intake and the prevalence of PD. In multivariable regression models adjusted for all confounding factors, compared with the lowest quartile of added sugars intake, the third quartile (OR = 2.99; 95% CI: 1.43–6.26) and those consuming more than 25% of their calories from added sugars (OR = 3.34; 95% CI: 1.03–10.86) had the highest risk of PD. The RCS curve showed an L-shaped nonlinear association between added sugars intake and PD. Two-segment linear regression by sex revealed that PD prevalence in women was linearly related to sugar intake (nonlinear P = 0.465), while men exhibited an L-shaped nonlinear relationship (nonlinear P = 0.03). Additionally, subgroup analysis showed that alcohol consumption and diabetes significantly influenced the association between added sugars intake and the prevalence of PD. These results highlight a positive association between added sugars intake and the prevalence of PD, particularly among women, heavy drinkers, and individuals with diabetes.

Background: The study aimed to examine the association between the systemic immune-inflammation index (SII), a contemporary metric of systemic inflammatory response, and biological aging, which are closely interconnected processes.Methods: This cross-sectional study utilized 10 cycles of data from the NHANES database spanning from 1990 to 2018. The study examined the relationship between the SII index, calculated as P * N/L, where P represents preoperative peripheral platelet count, N represents neutrophil count, and L represents lymphocyte count, and biological aging. Biological aging was assessed through various methods, such as phenotypic age, phenotypic age acceleration (PhenoAgeAccel), biological age, and biological age acceleration (BioAgeAccel). Correlations were analyzed using weighted linear regression and subgroup analysis.Results: Among the 7,491 participants analyzed, the average age was 45.26 ± 0.34 years, with 52.16% being female. The average phenotypic and biological ages were 40.06 ± 0.36 and 45.89 ± 0.32 years, respectively. Following adjustment for potential confounders, elevated SII scores were linked to increased phenotypic age, biological age, Phenotypic age acceleration, and Biological age acceleration. Positive correlations were observed between health behavior and health factor scores and biological aging, with stronger associations seen for health factors. In health factor-specific analyses, the β coefficient was notably higher for high BMI. The robust positive associations between SII scores and both phenotypic age and biological age in the stratified analyses were consistently observed across all strata.Conclusion: The evidence from the NHANES data indicate that SII may serve as a valuable marker for assessing different facets of aging and health outcomes, such as mortality and the aging process. Additional research is warranted to comprehensively elucidate the implications of SII in the aging process and its utility as a clinical instrument for evaluating and addressing age-related ailments.

This study was devised to evaluate the burden of breast cancer (BC) among adolescents and young adults (AYAs) between 1990 and 2021. BC burden among AYAs (15-39 years of age) was analyzed by examining the associated age-standardized incidence rate, age-standardized prevalence rate, age-standardized death rate, and age-standardized disability-adjusted life year (DALY) rates between 1990 and 2021 at the global, national, and regional levels, together with corresponding analyses of health inequality and decomposition analyses. In 2021, the respective global age-standardized incidence rate, age-standardized prevalence rate, age-standardized death rate, and age-standardized DALYs for BC were 5.87 (5.39,6.41), 49.35 (45.46,53.86), 1.37 (1.24,1.51), and 80.8 (72.84,89.23), which respectively showed increases of 33.4 %, 35.2 %, 1.5 %, and 3.3 % compared to the rate of 1990. While women comprise the vast majority of all BC cases, a higher average annual percent change was observed for males relative to females at the global level. The average annual percent change for BC incidence, prevalence, death, and DALYs among men AYAs (2.59, 2.49, 1.06, 1.10) were all higher than the corresponding values among women (0.91, 0.96, 0.02, 0.09). Regional and national differences in disease burden were observed. BC disease burden was significantly positively correlated with socio-demographic index (SDI). Similar trends were observed in terms of female disease burden, whereas disease burden of male patients was negatively related to SDI. In decomposition analyses, the primary factors associated with changes in age-standardized DALY rates were identified as aging and population growth. Health inequality analyses revealed that the burden of BC among AYAs was particularly concentrated in lower-income countries. These results highlight that the burden of BC among AYAs has grown in recent decades, while also emphasizing the need to be attentive to the rising occurrence of male BC. Lower-income regions also faced a heavier BC-related burden as compared to more affluent nations. Efforts to reduce the disease burden associated with BC may thus hinge on government-based initiatives focused on improving overall national economic strength and medical system quality.

Background: The pandemic of coronavirus disease 2019 (COVID-19) has become the first concern in international affairs as the novel coronavirus (SARS-CoV-2) is spreading all over the world at a terrific speed. The accuracy of early diagnosis is critical in the control of the spread of the virus. Although the real-time RT-PCR detection of the virus nucleic acid is the current golden diagnostic standard, it has high false negative rate when only apply single test. Objective: Summarize the baseline characteristics and laboratory examination results of hospitalized COVID-19 patients. Analyze the factors that could interfere with the early diagnosis quantitatively to support the timely confirmation of the disease. Methods: All suspected patients with COVID-19 were included in our study until Feb 9 ^th , 2020. The last day of follow-up was Mar 20 ^th , 2020. Throat swab real-time RT-PCR test was used to confirm SARS-CoV-2 infection. The difference between the epidemiological profile and first laboratory examination results of COVID-19 patients and non-COVID-19 patients were compared and analyzed by multiple logistic regression. Receiver operating characteristic (ROC) curve and area under curve (AUC) were used to assess the potential diagnostic value in factors, which had statistical differences in regression analysis. Results: In total, 315 hospitalized patients were included. Among them, 108 were confirmed as COVID-19 patients and 207 were non-COVID-19 patients. Two groups of patients have significance in comparing age, contact history, leukocyte count, lymphocyte count, C-reactive protein, erythrocyte sedimentation rate (p<0.10). Multiple logistic regression analysis showed age, contact history and decreasing lymphocyte count could be used as individual factor that has diagnostic value (p<0.05). The AUC of first RT-PCR test was 0.84 (95% CI 0.73-0.89), AUC of cumulative two times of RT-PCR tests was 0.92 (95% CI 0.88-0.96) and 0.96 (95% CI 0.93-0.99) for cumulative three times of RT-PCR tests. Ninety-six patients showed typical pneumonia radiological features in first CT scan, AUC was 0.74 (95% CI 0.60-0.73). The AUC of patients’ age, contact history with confirmed people and the decreased lymphocytes were 0.66 (95% CI 0.60-0.73), 0.67 (95% CI 0.61-0.73), 0.62 (95% CI 0.56-0.69), respectively. Taking chest CT scan diagnosis together with patients age and decreasing lymphocytes, AUC would be 0.86 (95% CI 0.82-0.90). The age threshold to predict COVID-19 was 41.5 years, with a diagnostic sensitivity of 0.70 (95% CI 0.61-0.79) and a specificity of 0.59 (95% CI 0.52-0.66). Positive and negative likelihood ratios were 1.71 and 0.50, respectively. Threshold of lymphocyte count to diagnose COVID-19 was 1.53×10 ⁹ /L, with a diagnostic sensitivity of 0.82 (95% CI 0.73-0.88) and a specificity of 0.50 (95% CI 0.43-0.57). Positive and negative likelihood ratios were 1.64 and 0.37, respectively. Conclusion: Single RT-PCR test has relatively high false negative rate. When first RT-PCR test show negative result in suspected patients, the chest CT scan, contact history, age and lymphocyte count should be used combinedly to assess the possibility of SARS-CoV-2 infection.