[The experimental study of diquat on the half-Lethal dose and pothological injuny of related organs in wistor rats].
Wu Y Z,Kan B T,Wang W J,Zhang Z C,Jia J E,Li X Q,Han J,Yu L J,Jian X D
Zhonghua lao dong wei sheng zhi ye bing za zhi = Zhonghua laodong weisheng zhiyebing zazhi = Chinese journal of industrial hygiene and occupational diseases
To explore the acute toxicity of Diquat in mice and to calculate the median lethal dose (LD(50)) of Diquat to rats and observe the pathological changes of tissues and organs in rats with different concentrations of Diquat. Diquat solution of 50 mg/kg was prepared freshly with 1 000 mg of Diquat and dilute the solution with water to a total of 20 ml. A total of 99 healthy adult male Wistar rats were randomly divided into part one, part two and control groups. In the first part, 36 rats were randomly divided into 4 groups: 100 mg/kg group, 200 mg/kg group, 300 mg/kg group and 400 mg/kg group, which were treated with 100 mg/kg, 200 mg/kg, 300 mg/kg and 400 mg/kg of Diquat solution by gavage, respectively. The death and symptoms of poisoning after intragastric administration were recorded, and the maximum tolerated dose and absolute lethal dose were measured. In the second part, 54 rats were randomly divided into 6 groups: 200 mg/kg group, 220 mg/kg group, 240 mg/kg group, 260 mg/kg、280 mg/kg group and 300 mg/kg group, whichwere treated with 200 mg/kg, 220 mg/kg, 240 mg/kg, 260 mg/kg, 280 mg/kg and 300 mg/kg of Diquat solution by gavage, respectively. The survival of rats in different concentration of Diquat was observed and the LD(50) was calculated by Excel processing the formula of Koch's method. The control group were given equal volume water under the same experimental conditions. And moreover, the lungs, kidneys, hearts, livers, and brain tissues were collected and fixed by formaldehyde, embedded by paraffin, and sectioned for histopathological light microscopy. The maximum tolerated dose was 240 mg/kg and the absolute lethal dose was 300 mg/kg. The LD(50) of Diquat for Rats was 280.58 mg/kg. The high-dose group had significantly more organ damage than the lowdose group after diquat poisoning. The determination of the half-lethal dose of diquat, at the same time observed multiple organs damaged in rats after the diquat quickly poisoned. Kidneys, lungs and heart might be the main organ which was heavily damaged. With the extension of observation time, the organ damage of rats exposed to small doses gradually stabilized.
10.3760/cma.j.issn.1001-9391.2018.11.004
Association of metabolic syndrome severity with frailty progression among Chinese middle and old-aged adults: a longitudinal study.
Cardiovascular diabetology
BACKGROUND:The binary diagnosis of Metabolic Syndrome(MetS) fails to accurately evaluate its severity, and the association between MetS severity and frailty progression remains inadequately elucidated. This study aims to clarify the relationship between the severity of MetS and the progression of frailty among the middle-aged and elderly population in China. METHOD:Participants from the 2011-2018 China Health and Retirement Longitudinal Study(CHARLS) were included for a longitudinal analysis. The study employs a frailty index(FI) based on 32 health deficits to diagnose frailty and to assess FI trajectories. An age-sex-ethnicity-specific MetS scoring model (MetS score) was used to assess metabolic syndrome severity in Chinese adults. The Cumulative MetS score from 2012 to 2015 was calculated using the formula: (MetS score in wave 1 + MetS score in wave 3) / 2 × time(2015 - 2012). The association between MetS score, Cumulative MetS score, and the risk and trajectory of frailty were evaluated using Cox regression/logistic regression, and linear mixed models. Restricted Cubic Splines(RCS) models were utilized to detect potential non-linear associations. RESULTS:A higher MetS score was significantly associated with an increased risk of frailty(HR per 1 SD increase = 1.205; 95%CI: 1.14 to 1.273) and an accelerated FI trajectory(β per 1 SD increase = 0.113 per year; 95%CI: 0.075 to 0.15 per year). Evaluating changes in MetS score using a Cumulative MetS score indicated that each 1 SD increase in the Cumulative MetS score increased the risk of frailty by 22.2%(OR = 1.222; 95%CI: 1.133 to 1.319) and accelerated the rate of increase in FI(β = 0.098 per year; 95%CI: 0.058 to 0.138 per year). RCS model results demonstrated a dose-response curve relationship between MetS score and Cumulative MetS score with frailty risk. Stratified analysis showed consistency across subgroups. The interaction results indicate that in males and individuals under aged 60, MetS score may accelerate the increase in FI, a finding consistent across both models. CONCLUSIONS:Our findings underscore the positive correlation between the severity of MetS and frailty progression in the middle-aged and elderly, highlighting the urgent need for early identification of MetS and targeted interventions to reduce the risk of frailty.
10.1186/s12933-024-02379-9
Associations of the glycaemic index and the glycaemic load with risk of type 2 diabetes in 127 594 people from 20 countries (PURE): a prospective cohort study.
The lancet. Diabetes & endocrinology
BACKGROUND:The association between the glycaemic index and the glycaemic load with type 2 diabetes incidence is controversial. We aimed to evaluate this association in an international cohort with diverse glycaemic index and glycaemic load diets. METHODS:The PURE study is a prospective cohort study of 127 594 adults aged 35-70 years from 20 high-income, middle-income, and low-income countries. Diet was assessed at baseline using country-specific validated food frequency questionnaires. The glycaemic index and the glycaemic load were estimated on the basis of the intake of seven categories of carbohydrate-containing foods. Participants were categorised into quintiles of glycaemic index and glycaemic load. The primary outcome was incident type 2 diabetes. Multivariable Cox Frailty models with random intercepts for study centre were used to calculate hazard ratios (HRs). FINDINGS:During a median follow-up of 11·8 years (IQR 9·0-13·0), 7326 (5·7%) incident cases of type 2 diabetes occurred. In multivariable adjusted analyses, a diet with a higher glycaemic index was significantly associated with a higher risk of diabetes (quintile 5 vs quintile 1; HR 1·15 [95% CI 1·03-1·29]). Participants in the highest quintile of the glycaemic load had a higher risk of incident type 2 diabetes compared with those in the lowest quintile (HR 1·21, 95% CI 1·06-1·37). The glycaemic index was more strongly associated with diabetes among individuals with a higher BMI (quintile 5 vs quintile 1; HR 1·23 [95% CI 1·08-1·41]) than those with a lower BMI (quintile 5 vs quintile 1; 1·10 [0·87-1·39]; p interaction=0·030). INTERPRETATION:Diets with a high glycaemic index and a high glycaemic load were associated with a higher risk of incident type 2 diabetes in a multinational cohort spanning five continents. Our findings suggest that consuming low glycaemic index and low glycaemic load diets might prevent the development of type 2 diabetes. FUNDING:Full funding sources are listed at the end of the Article.
10.1016/S2213-8587(24)00069-X
Associations of metabolic heterogeneity of obesity with frailty progression: Results from two prospective cohorts.
Journal of cachexia, sarcopenia and muscle
BACKGROUND:Previous studies indicated that obesity would accelerate frailty progression. However, obesity is heterogeneous by different metabolic status. The associations of metabolic heterogeneity of obesity with frailty progression remain unclear. METHODS:A total of 6730 participants from the China Health and Retirement Longitudinal Study (CHARLS) and 4713 from the English Longitudinal Study of Ageing (ELSA) were included at baseline. Metabolic heterogeneity of obesity was evaluated based on four obesity and metabolic phenotypes as metabolically healthy normal weight (MHNW), metabolically unhealthy normal weight (MUNW), metabolically healthy overweight/obesity (MHOO), and metabolically unhealthy overweight/obesity (MUOO). Frailty status was assessed by the frailty index (FI) ranging from 0 to 100 and frailty was defined as FI ≥ 25. Linear mixed-effect models were used to analyse the associations of metabolic heterogeneity of obesity with frailty progression. RESULTS:In the CHARLS, MUOO and MUNW presented the accelerated FI progression with additional annual increases of 0.284 (95% CI: 0.155 to 0.413, P < 0.001) and 0.169 (95% CI: 0.035 to 0.303, P = 0.013) as compared with MHNW. MHOO presented no accelerated FI progression (β: -0.011, 95% CI: -0.196 to 0.173, P = 0.904) as compared with MHNW. In the ELSA, the accelerated FI progression was marginally significant for MUOO (β: 0.103, 95% CI: -0.005 to 0.210, P = 0.061) and MUNW (β: 0.157, 95% CI: -0.011 to 0.324, P = 0.066), but not for MHOO (β: -0.047, 95% CI: -0.157 to 0.062, P = 0.396) in comparison with MHNW. The associations of MUOO and MUNW with the accelerated FI progression were stronger after excluding the baseline frail participants in both cohorts. The metabolic status changed over time. When compared with stable MHNW, participants who changed from MHNW to MUNW presented the accelerated FI progression with additional annual increases of 0.356 (95% CI: 0.113 to 0.599, P = 0.004) and 0.255 (95% CI: 0.033 to 0.477, P = 0.024) in the CHARLS and ELSA, respectively. The accelerated FI progression was also found in MHOO participants who transitioned to MUOO (CHARLS, β: 0.358, 95% CI: 0.053 to 0.663, P = 0.022; ELSA, β: 0.210, 95% CI: 0.049 to 0.370, P = 0.011). CONCLUSIONS:Metabolically unhealthy overweight/obesity and normal weight, but not metabolically healthy overweight/obesity, accelerated frailty progression as compared with metabolically healthy normal weight. Regardless of obesity status, transitions from healthy metabolic status to unhealthy metabolic status accelerated frailty progression as compared with stable metabolically healthy normal weight. Our findings highlight the important role of metabolic status in frailty progression and recommend the stratified management of obesity based on metabolic status.
10.1002/jcsm.13169