| BackgroundDementia,an age-related neurodegenerative disease,severely affected the quality of life and nutritional status of patients,greatly increased the demand for health services worldwide.However,the prevention and treatment of dementia remains a challenge for modern medicine.Given the difficulty of treating dementia and the irreversibility of the disease,identifying its potentially modifiable risk factors is important to prevent the development of dementia and reduce the burden of dementia.Body weight is thought to be associated with dementia risk,however,the relationship between weight and cognitive function and dementia risk has been controversial in past studies.As a population with a high prevalence of cognitive impairment and dementia,older adults are often exposed to weight changes and fluctuations caused by various factors.Therefore,when considering the relationship between weight and cognitive function,it is necessary to focus our attention on the elderly stage.As an indicator of appetite and musculoskeletal level,the longitudinal fluctuations of body weight reflects and influences the physical condition of older adults.However,there is a lack of reasonable goals for weight control in the elderly,and the effects of weight fluctuations cannot be generalized among older adults with different body masses.Therefore,it is worth exploring the relationship between body weight fluctuation and cognitive function changes in the older age.PurposeIn this study,we used the Alzheimer’s disease neuroimaging initiative(ADNI)database,an open longitudinal multicenter database in the United States,to analyze the relationship between weight fluctuation trends and cognitive function in older adults,and to explore the differences in the relationship between weight fluctuation trends and cognitive function in older adults with different body mass types to provide a clinical reference for weight control and dementia prevention in older adults.Methods1.Data sourcesAll data used in this study were obtained from the ADNI database.The analysis information of participants included demographic characteristics,clinical characteristics,medical history,vital signs and neuropsychological tests.2.Study populationWe screened participants who were followed up at 24 months in the ADNI cohort.After excluding participants with a baseline diagnosis of Alzheimer’s disease(AD),missing information on important variables(including diagnosis information,height,weight,and disease history),age<60 years,and low weight(BMI<18.5 kg/m2),1,058 eligible participants were finally included.3.Cognitive status assessmentCognitive status was classified into:cognitive normal(CN),mild cognitive impairment(MCI)and AD.The study outcome was defined as progression of cognitive impairment in participants.For participants with a baseline diagnosis of CN,progression was defined if the diagnosis converted into MCI or AD at 24 months;for participants with a baseline diagnosis of MCI,progression was defined if the diagnosis was converted to AD at 24 months.We used the 24 month change(Δ CDR)in the Clinical Dementia Rating(CDR)to assess changes in cognitive function among participants.CDR is one of the most commonly used tools in clinical assessment of AD-type dementia.4.Weight fluctuation status assessmentThe body mass index(BMI)was calculated using the formula:BMI=weight(kg)/height2(m2).Participants were divided according to baseline BMI:normal(18.5 kg/m2<BMI<25 kg/m2),overweight(25 kg/m2<BMI<30 kg/m2)and obese(BMI≥30 kg/m2).A small number of underweight(BMI<18.5 kg/m2)participants were excluded.The weight change rate(WCR)was calculated using the formula:WCR=[24-month follow-up weight(kg)-baseline weight(kg)]/baseline weight(kg)*100%.Participants were classified into the following three weight fluctuation status according to WCR:weight loss(WCR ≤-5%),weight stability(-5%<WCR<5%)and weight gain(WCR≥5%).5.CovariatesCovariates relevant to this study were included in the model.Demographic information included age,gender,education,ethnicity,race and status of married.Clinical characteristics included smoking,alcohol abuse,APOEε4 carriage,baseline diagnosis,hypertension,diabetes,hypercholesterolemia,coronary artery disease,stroke and depression.6.Statistical analysisMultivariate logistic regression models was used to evaluate the relationship between BMI and weight fluctuation status and the progression of cognitive impairment.Multivariate linear regression analysis was used to evaluate the relationship between BMI and weight fluctuation status and changes in neuropsychological test scores.Restricted cubic spline curve was used to analyze the non-linear relationship between BMI and weight change rate and cognitive impairment progression.A two-tailed P value<0.05 was considered statistically significant.Results1.Participants characteristicsA total of 1058 participants with a mean age of 73.9(±6.5)years were included in the study,633 of whom were diagnosed with CN and 425 of whom were diagnosed with MCI at baseline.The proportion of MCI participants was higher than CN participants in all three weight fluctuation status groups(P=0.024).Participants in the weight stability group had higher baseline weight(P=0.001)and BMI(P=0.002)compared to participants in the weight fluctuation groups.2.Association of BMI type and weight fluctuation status with the progression of cognitive impairmentCompared to participants with normal BMI,overweight and obesity were not significantly associated with the risk of cognitive impairment progression.Participants with fluctuating weight status were both associated with a higher risk of cognitive impairment progression compared to the stable weight group(weight loss:OR=1.62,CI=1.06-2.49,P=0.027;weight gain:OR=1.72,CI=1.05-2.83,P=0.032).Further stratified analysis by baseline BMI type found that among older adults with normal baseline BMI,the relationship between weight change status and cognitive function was consistent with the overall population results(decrease:OR=2.29,CI=1.05-5.04,p=0.039;increase:OR=2.52,CI=1.20-5.29,p=0.015).However,for both overweight and obese individuals,although both weight fluctuation groups had higher rates of cognitive impairment progression than the weight stable group(overweight:24.0%&19.0%vs.17.8%;obese:17.9%&16.7%vs.13.3%),there was no statistically significant difference in the risk of cognitive impairment progression between different weight fluctuation types.3.The Non-linear relationship between BMI and weight change rate and the progression of cognitive impairmentThe relationship between BMI level and weight change rate and risk of cognitive impairment progression was further analyzed using restricted cubic spline graph.There was no significant correlation between BMI level and risk of progression of cognitive impairment(P for non-liner=0.519),while there was a nonlinear relationship between the weight change rate and the risk of cognitive impairment progression(P for non-liner=0.002).When weight gain reached around 6%or weight loss reached around 3%,the risk of cognitive impairment progression gradually increased as weight change further increased.4.Stratified analysis of the relationship between weight fluctuation status and cognitive impairment progression by age and geneticsAfter stratified analysis,the association between weight fluctuation and the risk of cognitive impairment progression was observed in women(weight loss:OR=2.01,CI=0.97-4.15,P=0.060;weight gain:OR=3.57,CI=1.68-7.59,P=0.001)and APOEε4 non-carriers(weight loss:OR=2.16,CI=1.12-4.15,P=0.002;weight gain:OR=2.05,Cl=0.93-4.53,P=0.077).5.Association between BMI type and weight fluctuation status with CDR scores changesAnalysis revealed that weight fluctuation status was associated with increased CDR scores at 2 years(weight loss:B=0.22,CI=-0.01-0.44,P=0.062;weight gain:B=0.39,CI=0.11-0.66,P=0.006).In addition,obesity at baseline was significantly associated with reduced CDR scores(B=-0.26,CI=-0.050-0.01,P=0.041).ConclusionThe results of this study suggest that weight fluctuations in older adults are associated with a higher risk of cognitive decline.In addition,high BMI may have some cognitive protective effects.For elderly people with normal BMI,attention should be paid to weight monitoring and maintaining weight stability.For elderly people with excessive weight and high BMI,necessary weight loss should be carried out slowly and steadily.Our study extends the observations of previous cohorts,provides further explanation of the relationship between weight and cognitive function,and provides some reference for setting a reasonable target weight for older adults.BackgroundMultiple myeloma(MM)is the second most common hematologic malignancy.Despite significant advances in the treatment of MM,the disease remains incurable and patients often face recurrent disease and repeated hospitalizations.As can be expected,repeat hospitalizations for MM patients place a significant burden on patients and health care systems.Although readmission is strongly associated with disease outcomes and health care burden in MM patients,studies on readmission are very limited.To clarify the factors influencing the heterogeneity of readmission in MM patients and to reduce the ongoing healthcare burden associated with readmission in MM patients,there is an urgent need to identify potential risk factors for readmission in MM patients.Obesity is an urgent and growing global public health threat and is an important risk factor for many chronic diseases.However,past evidence suggests that the relationship between obesity and MM remains controversial.Obesity is often accompanied by a range of metabolic abnormalities,and the combined metabolic abnormalities in obese patients may influence the relationship between obesity and disease as well as the disease itself.Given this feature of obesity and metabolic abnormalities and the conflicting evidence for the relationship between obesity and MM,consideration of the combined role of obesity and metabolism could lead to more effective identification of risk factors for MM.PurposeIn order to accurately and systematically explore potentially modifiable risk factors for MM,we conducted a retrospective cohort study using the Nationwide Readmissions Database(NRD),a large contemporary national database in the United States,to explore the relationship between obesity metabolic phenotype association with risk of readmission for MM.MethodsThe study included 34,852 participants from NRD2018 who were diagnosed with MM at the time of their initial hospitalization.Modification,ICD-10-CM)diagnosis codes were obtained.Obesity was defined as a BMI≥25 kg/m2.Metabolic unhealthiness was defined as having two or more of the following metabolic risk factors:(1)hypertension:primary or secondary hypertension or undiagnosed elevated blood pressure;(2)dyslipidemia:high serum triglyceride levels or HDL cholesterol levels,etc.;(3)hyperglycemia:pre-diabetes or diabetes or other specific diabetes.Based on obesity and metabolic status,the population is divided into four phenotypes:metabolically healthy non-obese(MHNO),metabolically unhealthy non-obese(MUNO),metabolically healthy obese(MUNO),and metabolically healthy obese(MUNO).Metabolically healthy obese,MHO),and Metabolically unhealthy obese(MUO).The primary outcome was unplanned readmission within 30,60,90,and 180 days after initial discharge.Multivariate COX regression models were used to estimate the relationship between obesity metabolic phenotype and readmission risk.Results1.Baseline characteristicsThis study included 34,852 patients from NDR2018 who were diagnosed with MM at first discharge.For the 30-,60-,90-,and 180-day follow-up cohorts,34,852,32,344.29,747,and 22,032 participants were analyzed,respectively.The mean age of the population was 69.8 years,with 69.4%being 65 years or older and 55.3%being male.There were 4718(13.5%)patients with obesity and 15417(44.2%)patients with metabolic ill health.the MHNO group was the largest of the four phenotypes,while the MHO group had the longest mean length of stay(9.5 days)and the highest mean total cost($112,913.9).The MUNO and MUO groups had more elderly patients(>65 years)with higher rates of heart failure,renal failure and coronary heart disease compared to the MHNO and MHO groups.2.Risk of readmissionUnplanned readmissions occurred in 5400(15.5%),7255(22.4%),8025(27.0%),and 7839(35.6%)at 30-,60-,90-,and 180-day follow-ups,respectively.At 90-and 180-day follow-up,patients with the metabolically unhealthy phenotypes MUNO(90 days:P=0.004;180 days:P=<0.001)and MUO(90 days:P=0.049;180 days:P=0.004)showed a higher risk of readmission compared with patients with the MHNO phenotype,while patients with the obesity-only phenotype MHO(90 days:P=0.170;180 days:P=0.090)did not show a higher risk of readmission.However similar results were not observed at 30-and 60-day follow-up.Further analysis of the 90-day follow-up showed that the risk of readmission increased with the number of combined abnormal metabolic factors,with HRs of 1.068(95%CI:1.002-1.137,P=0.043,one metabolic risk factor),1.109(95%CI:1.038-1.184,P=0.002,two metabolic risk factors)and 1.125(95%Cl:1.04-1.216,P=0.003,three metabolic risk factors).3.Mortality and medical burden during readmissionWe observed that MUO patients were more likely to be hospitalized for longer periods than MHNO patients during 30-,60-and 90-day readmissions(30 days:36.3%vs.28.5%,p<0.05;60 days:35.1%vs.28.1%;90 days:32.9%vs.27.3%,p<0.05).ConclusionMetabolic abnormalities,but not obesity,were independently associated with an increased risk of readmission in patients with MM.Furthermore,the risk of readmission in patients with MM increases with the number of comorbid metabolic risk factors.In the management of MM,clinical interventions should not only focus on the patient’s obesity but also increase the attention to metabolic abnormalities.For MM patients with combined metabolic risk factors,clinical attention should be paid to early intervention and optimal care to reduce the risk of readmission and reduce the burden of hospitalization. |
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