Objective:This study aims to evaluate the subcortical structural alteration patterns associated with five distinct neuropsychiatric symptoms (NPS) associated with Parkinson′s disease and provide macroscopic brain structural evidence to further explore their underlying pathophysiological mechanism.Methods:Clinical data and 3D-T 1 weighted images from 505 patients with Parkinson′s disease and 167 age-and sex-matched healthy controls were obtained from the Parkinson′s Progression Markers Initiative database (July 2010-August 2022). The subcortical nucleus volumes of the overall NPS patient group, as well as subgroups of patients with specific NPS subtypes (depression, anxiety, apathy, impulsive-compulsive behaviors (ICBs), and hallucinations), were measured and compared to those of healthy controls through mixed-effects models. Spatial similarity analysis and hierarchical clustering analysis of subcortical volume alteration patterns were employed to investigate the commonalities and specificities of subcortical damage in NPS. Results:NPS patients exhibited widespread subcortical atrophy, primarily concentrated in the bilateral putamen, bilateral hippocampus, and left amygdala (Cohen′s d=-0.38--0.12, FDR P<0.05). Subgroup analysis revealed that anxiety and depression were associated with gray matter atrophy in the bilateral putamen and amygdala (Cohen′s d=-0.73--0.32, FDR P<0.05), while apathy, hallucinations, and ICBs were linked to atrophy in the bilateral putamen, bilateral amygdala, and bilateral hippocampus (Cohen′s d=-0.61--0.11, FDR P<0.05,Cohen′s d=-0.43--0.36, P<0.05). Similarity and clustering analyses demonstrated high spatial correlation between anxiety and depression ( r=0.83, P<0.01), forming one cluster, whereas apathy, hallucinations, and ICBs formed another distinct cluster. Conclusion:NPS in Parkinson′s disease exhibit both commonalities and specificities. Apathy, hallucinations, and ICBs are associated with more severe subcortical damage patterns. These findings may provide new insights into the pathophysiology and progression of Parkinson′s disease.

Objective:This study aims to evaluate the subcortical structural alteration patterns associated with five distinct neuropsychiatric symptoms (NPS) associated with Parkinson′s disease and provide macroscopic brain structural evidence to further explore their underlying pathophysiological mechanism.Methods:Clinical data and 3D-T 1 weighted images from 505 patients with Parkinson′s disease and 167 age-and sex-matched healthy controls were obtained from the Parkinson′s Progression Markers Initiative database (July 2010-August 2022). The subcortical nucleus volumes of the overall NPS patient group, as well as subgroups of patients with specific NPS subtypes (depression, anxiety, apathy, impulsive-compulsive behaviors (ICBs), and hallucinations), were measured and compared to those of healthy controls through mixed-effects models. Spatial similarity analysis and hierarchical clustering analysis of subcortical volume alteration patterns were employed to investigate the commonalities and specificities of subcortical damage in NPS. Results:NPS patients exhibited widespread subcortical atrophy, primarily concentrated in the bilateral putamen, bilateral hippocampus, and left amygdala (Cohen′s d=-0.38--0.12, FDR P<0.05). Subgroup analysis revealed that anxiety and depression were associated with gray matter atrophy in the bilateral putamen and amygdala (Cohen′s d=-0.73--0.32, FDR P<0.05), while apathy, hallucinations, and ICBs were linked to atrophy in the bilateral putamen, bilateral amygdala, and bilateral hippocampus (Cohen′s d=-0.61--0.11, FDR P<0.05,Cohen′s d=-0.43--0.36, P<0.05). Similarity and clustering analyses demonstrated high spatial correlation between anxiety and depression ( r=0.83, P<0.01), forming one cluster, whereas apathy, hallucinations, and ICBs formed another distinct cluster. Conclusion:NPS in Parkinson′s disease exhibit both commonalities and specificities. Apathy, hallucinations, and ICBs are associated with more severe subcortical damage patterns. These findings may provide new insights into the pathophysiology and progression of Parkinson′s disease.

Objective: To estimate the prevalence of MS in elderly people aged ≥60 years and its related factors in China and provide scientific evidence for prevention and control of MS in the elderly. Methods: Data used in this study were obtained from the 2013 Chinese Chronic Diseases and Risk Factor Surveillance Program. A total of 50 497 people aged ≥60 years were selected and interviewed through multistage stratified cluster sampling at 298 surveillance sites in 31 provinces. According to the Chinese MS diagnostic criteria proposed by the Chinese Medical Association Diabetes Branch in 2017, the prevalence rates of different MS forms were compared, and the main related factors were analyzed. Results: The prevalence rate of MS was 36.9% (95%CI: 35.4-38.5). The prevalence rate was higher in urban area than in the rural area, higher in females than in males, higher in eastern area than in western area. The prevalence rate of MS in elderly people aged ≥70 years was lower than that in those aged 60-69 years. The rate in the elderly with higher education and income levels was higher than that in the elderly with lower socioeconomic level. The comparison of the prevalence of the five forms of MS in the elderly showed that hypertension had the highest prevalence rate (72.8%), followed by hyperglycemia (41.7%) and central obesity (37.6%). The prevalence rates of hypertriglyceridemia and low HDL-C were 25.8% and 17.5%. The risk for MS in women was 1.20 times higher than that in men. Age, gender, education level, living area and urban or rural residence were the main factors influencing the prevalence of MS. Smoking, drinking and physical activity levels were correlated with MS. Conclusions The risk for MS was higher in women than in men in China’s elderly population, and the risk was related to socioeconomic level and life behaviors. It is recommended to carry out lifestyle interventions, such as increasing exercise and having reasonable diet for the elderly patients with MS. Hypertension and diabetes patients also need to be treated with drugs to reduce the risk of cardiovascular disease morbidity and mortality.

Objective: To understand the control attempts of body weight and its related factors among overweight and obese adults in China. Methods: Data was from the 2013 Chinese Chronic Diseases and Risk Factors Surveillance Program, which covered 302 surveillance sites. 179 570 adults, selected through multistage stratified cluster sampling method, were interviewed. Demographic characteristics and weight-control attempts were collected via face-to-face interview. BMI, waist circumstance and blood pressure were individually measured under physical examination. Venous blood samples were obtained and tested for FPG, OGTT-2h, TC, TG, LDL-C and HDL-C. A total of 87 545 overweight and obese patients were included in this study, with the exclusion of 152 patients having the missed critical information. Rates on weight control and attempts were analyzed, using the complex weighting on samples to represent the overall overweight and obese adults in China. Results: The rate of weight-control attempts was 16.3% (95%CI: 14.9%-17.7%). Among all the 12 133 patients who had undergone weight-control measures, the proportions of different attempts were as follows: diet (40.9%, 95%CI: 38.4%-43.3%), combination of diet and physical activity (31.5%, 95%CI: 28.9%-34.0%), physical activity (22.8%, 95%CI: 21.0%-24.6%) and drug control (1.3%, 95%CI: 1.0%-1.7%). Factors as: being female (OR=1.26, 95%CI: 1.15-1.38), at younger age (18-44 years old, OR=1.51, 95%CI: 1.31-1.74), with high education levels (college degree or above, OR=4.52, 95%CI: 3.76-5.43), having high annual income (≥24 000 Yuan, OR=1.94, 95%CI: 1.63-2.30) etc., appeared as favorable factors for taking the measures vs. rural residency (OR=0.63, 95%CI: 0.55- 0.72) as the unfavorable one. Conclusion The rate of weight-control attempts appeared low among the overweight and obese adults who were affected by factors as age, education and income level. Personalized intervention measures should be carried out for people with different characteristics.