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The risk of Alzheimer's disease according to dynamic changes in metabolic health and obesity: a nationwide population-based cohort study. Aging (Albany NY) 2021; 13:16974-16989. [PMID: 34237705 PMCID: PMC8312469 DOI: 10.18632/aging.203255] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Accepted: 06/18/2021] [Indexed: 12/29/2022]
Abstract
We evaluated the association of metabolic health and obesity phenotypes with the risk of Alzheimer's disease (AD). This study enrolled 136,847 elderly participants aged 60 or above from the Korean National Health Insurance System. At baseline examinations in 2009 and 2010, subjects were categorized into four groups: the metabolically healthy non-obese (MHNO), metabolically healthy obese (MHO), metabolically unhealthy non-obese (MUNO), and metabolically unhealthy obese (MUO) groups. Based on the phenotypic transition after 2 years, the subjects were further categorized into 16 subgroups. They were followed from 2009 to 2015 to monitor for AD development. The MHO phenotype protected subjects from AD, relative to the MHNO phenotype (HR, 0.73; 95% CI, 0.65-0.81). Among subjects initially classified as MHO, 41.8% remained MHO, with a significantly lower risk of AD compared with the stable MHNO group (HR, 0.62; 95% CI, 0.50-0.77). Among MUO subjects at baseline, those who changed phenotype to MUNO were at higher risk of AD (HR, 1.47; 95% CI, 1.28-1.70), and the transition to the MHO phenotype protected subjects from AD (HR, 0.62; 95% CI, 0.50-0.78). The MHO phenotype conferred a decreased risk of AD. Maintenance or recovery of metabolic health might mitigate AD risk among obese individuals.
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Zhang N, Liang G, Liu M, Zheng G, Yu H, Shi Y, Zhang Y, Wang H, Li Y, Xu Y, Lu J. Metabolically healthy obesity increases the prevalence of stroke in adults aged 40 years or older: Result from the China National Stroke Screening survey. Prev Med 2021; 148:106551. [PMID: 33862034 DOI: 10.1016/j.ypmed.2021.106551] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/27/2020] [Revised: 02/16/2021] [Accepted: 04/11/2021] [Indexed: 12/29/2022]
Abstract
Debate over the cardio-cerebrovascular risk associated with metabolically healthy obesity (MHO) continues. In this study we investigated the association of MHO with the risk of stroke among 221,114 individuals aged 40 years or older based on data from the China National Stroke Screening and Prevention Project (CNSSPP), a nationally representative cross-sectional study, during 2014 to 2015. Different metabolic health and obesity phenotypes were defined according to the Adult Treatment Panel III (ATP III) criteria, where obesity was defined as a body mass index (BMI) ≥28 kg/m2. Logistic regression models were used to estimate the odds ratios (ORs) and 95% confidence intervals (CIs) for stroke risk associated with different metabolically healthy phenotypes. BMI was used to estimate the mediation effect for metabolic abnormalities to stroke. Compared with the metabolically healthy non-obesity (MHNO) group, individuals with MHO (adjusted OR: 1.21, 95% CI: 1.10,1.33), metabolically unhealthy non-obesity (MUNO) (adjusted OR:1.41, 95% CI: 1.36,1.46), or metabolically unhealthy obesity (MUO) (adjusted OR: 1.70, 95% CI: 1.61,1.80) were found to have an increased risk of stroke. The findings were confirmed robustly by various sensitivity analyses and subgroup analyses. Furthermore, obesity and metabolic abnormalities had an additive interaction for stroke risk with an attributable proportion (AP) of 14.0% in females. BMI played a partial mediating role with the proportion of the effect (PE) at 11.1% in the relationship between metabolic abnormalities and stroke. This study strengthens the evidence that management and interventions in the MHO population may contribute to the primary prevention of stroke.
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Affiliation(s)
- Ningning Zhang
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan Province, China
| | - Ge Liang
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan Province, China
| | - Mengying Liu
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan Province, China
| | - Guowei Zheng
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan Province, China
| | - Hailan Yu
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan Province, China
| | - Yage Shi
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan Province, China
| | - Yihe Zhang
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan Province, China
| | - Hao Wang
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan Province, China
| | - Yapeng Li
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan Province, China
| | - Yuming Xu
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan Province, China
| | - Jie Lu
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan Province, China.
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Wang W, He J, Hu Y, Song Y, Zhang X, Guo H, Wang X, Keerman M, Ma J, Yan Y, Zhang J, Ma R, Guo S. Comparison of the Incidence of Cardiovascular Diseases in Weight Groups with Healthy and Unhealthy Metabolism. Diabetes Metab Syndr Obes 2021; 14:4155-4163. [PMID: 34621129 PMCID: PMC8491784 DOI: 10.2147/dmso.s330212] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Accepted: 09/09/2021] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND We aimed to identify the relationship between metabolically healthy obesity (MHO), a special subtype of obesity, and the incidence of cardiovascular disease (CVD) in rural Xinjiang. METHODS Body mass index (BMI) and the Joint Interim Statement criteria were utilized to define obesity and metabolic status, respectively. A baseline survey was conducted between 2010 and 2012. The cohort was followed-up until 2017, including 5059 participants (2953 Uyghurs and 2106 Kazakhs) in the analysis. RESULTS During 6.78 years of follow-up, 471 individuals developed CVD, 10.8% (n=545) of whom were obese, and the prevalence of MHO and MHNW was 5.2% and 54.5%, respectively. Compared with metabolically healthy normal weight subjects, the subjects with MHO had an increased risk of CVD (hazard ratio [HR]=1.76, 95% confidence interval [CI]: 1.23-2.51), while the metabolically unhealthy obesity (MUO) group had an even higher risk (HR=3.80, 95% CI: 2.87-5.03). Additionally, there were sex differences in the relationship between BMI-metabolic status and incident CVD (P interaction =0.027). Compared with the subjects with MHO, those with MUO had an increased risk of CVD (HR=1.84, 95% CI: 1.26-2.71). CONCLUSION MHO was associated with a high risk of CVD among adults in rural Xinjiang. In each BMI category, metabolically unhealthy subjects had a higher risk of developing CVD than did metabolically healthy subjects.
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Affiliation(s)
- Wenqiang Wang
- Department of Public Health, Shihezi University School of Medicine, Shihezi, Xinjiang, People’s Republic of China
| | - Jia He
- Department of Public Health, Shihezi University School of Medicine, Shihezi, Xinjiang, People’s Republic of China
| | - Yunhua Hu
- Department of Public Health, Shihezi University School of Medicine, Shihezi, Xinjiang, People’s Republic of China
| | - Yanpeng Song
- Department of Social Work, The First Affiliated Hospital of Shihezi University Medical College, Shihezi, Xinjiang, People’s Republic of China
| | - Xianghui Zhang
- Department of Public Health, Shihezi University School of Medicine, Shihezi, Xinjiang, People’s Republic of China
| | - Heng Guo
- Department of Public Health, Shihezi University School of Medicine, Shihezi, Xinjiang, People’s Republic of China
| | - Xinping Wang
- Department of Public Health, Shihezi University School of Medicine, Shihezi, Xinjiang, People’s Republic of China
| | - Mulatibieke Keerman
- Department of Public Health, Shihezi University School of Medicine, Shihezi, Xinjiang, People’s Republic of China
| | - Jiaolong Ma
- Department of Public Health, Shihezi University School of Medicine, Shihezi, Xinjiang, People’s Republic of China
| | - Yizhong Yan
- Department of Public Health, Shihezi University School of Medicine, Shihezi, Xinjiang, People’s Republic of China
| | - Jingyu Zhang
- Department of Public Health, Shihezi University School of Medicine, Shihezi, Xinjiang, People’s Republic of China
| | - Rulin Ma
- Department of Public Health, Shihezi University School of Medicine, Shihezi, Xinjiang, People’s Republic of China
- Rulin Ma Department of Public Health, The Key Laboratory of Preventive Medicine, Building No. 1, Shihezi University School of Medicine, Suite 816, Beier Road, Shihezi, 832000, Xinjiang, People’s Republic of ChinaTel +86-1330-9930-561Fax +86-993-2057-153 Email
| | - Shuxia Guo
- Department of Public Health, Shihezi University School of Medicine, Shihezi, Xinjiang, People’s Republic of China
- Department of NHC Key Laboratory of Prevention and Treatment of Central Asia High Incidence Diseases, The First Affiliated Hospital of Shihezi University Medical College, Shihezi, Xinjiang, People’s Republic of China
- Correspondence: Shuxia Guo Department of Public Health, The Key Laboratory of Preventive Medicine, Building No. 1, Shihezi University School of Medicine, Suite 721, Beier Road, Shihezi, 832000, Xinjiang, People’s Republic of ChinaTel +86-1800-9932-625Fax +86-993-2057-153 Email
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Hajian-Tilaki K, Heidari B. Metabolically healthy obese and unhealthy normal weight in Iranian adult population: Prevalence and the associated factors. Diabetes Metab Syndr 2018; 12:129-134. [PMID: 29196231 DOI: 10.1016/j.dsx.2017.11.005] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/27/2017] [Accepted: 11/22/2017] [Indexed: 01/01/2023]
Abstract
AIMS The objective of this study was to determine the prevalence and the associated factors of metabolically unhealthy in normal-weight and metabolically healthy in obese. METHODS We analyzed the data of a representative sample of 986 participants recruited among adult population of north of Iran. Data were collected regarding demographic characteristics, lifestyle, body mass index, abdominal obesity measures, blood pressure, and lipid profiles. The participants were classified as metabolically healthy obese (MHO) and metabolically unhealthy normal-weight (MUNW). Metabolically unhealthy was defined as the presence of ≥2 non-obese components of metabolic syndrome based on ATP III criteria. RESULTS The prevalence rate of MUNW and MHO accounted for 17.2% and 15.1% respectively. Mean age of participants with metabolically unhealthy was significantly greater than metabolically healthy in both normal weight and overweight/obese (P=0.001). The results of multiple logistic regression analysis showed in normal-weight individuals, a significant association of MUNW was found with age group of 50-59 years(OR=3.83, 95%CI: 1.71-8.57) and 60-70 years by OR=4.74(95%CI:1.79-12.54) as compared with age group of 20-29 years. It was also associated with current smoking. While metabolically healthy state in overweight/obese was inversely associated with age 50-59 years by OR=0.26 (95%CI:0.13-0.54) and age 60-70 years by OR=0.15 (95%CI:0.05- 0.39) and higher WC by OR=0.47 (95%CI:0.31-0.72) but positively associated with female-sex by OR=1.74 (95%CI:1.07-2.82). CONCLUSION Aging and smoking are significantly associated with metabolic abnormalities in normal-weight while aging, abdominal obesity negatively and female positively associated with metabolically healthy in obese.
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Affiliation(s)
| | - Behzad Heidari
- Dept of Internal Medicine, Ayatollah Rohani hospital, Babol University of Medical Sciences, Babol, Iran
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Comparison of body mass index, waist circumference, and waist to height ratio in the prediction of hypertension and diabetes mellitus: Filipino-American women cardiovascular study. Prev Med Rep 2016; 4:608-613. [PMID: 27882291 PMCID: PMC5118592 DOI: 10.1016/j.pmedr.2016.10.003] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2016] [Revised: 10/09/2016] [Accepted: 10/18/2016] [Indexed: 01/18/2023] Open
Abstract
The relative ability of three obesity indices to predict hypertension (HTN) and diabetes (DM) and the validity of using Asian-specific thresholds of these indices were examined in Filipino-American women (FAW). Filipino-American women (n = 382), 40–65 years of age were screened for hypertension (HTN) and diabetes (DM) in four major US cities. Body mass index (BMI), waist circumference (WC) and waist circumference to height ratio (WHtR) were measured. ROC analyses determined that the three obesity measurements were similar in predicting HTN and DM (AUC: 0.6–0.7). The universal WC threshold of ≥ 35 in. missed 13% of the hypertensive patients and 12% of the diabetic patients. The Asian WC threshold of ≥ 31.5 in. increased detection of HTN and DM but with a high rate of false positives. The traditional BMI ≥ 25 kg/m2 threshold missed 35% of those with hypertension and 24% of those with diabetes. The Asian BMI threshold improved detection but resulted in a high rate of false positives. The suggested WHtR cut-off of ≥ 0.5 missed only 1% of those with HTN and 0% of those with DM. The three obesity measurements had similar but modest ability to predict HTN and DM in FAW. Using Asian-specific thresholds increased accuracy but with a high rate of false positives. Whether FAW, especially at older ages, should be encouraged to reach these lower thresholds needs further investigation because of the high false positive rates. WC, BMI and WHtR measurements cutoff points were higher in middle aged FAW. WC, BMI and WHtR measurements had similar ability to predict HTN and DM in FAW. Need to tailor thresholds of obesity measurements for specific Asian subgroups.
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Abstract
[This corrects the article DOI: 10.1097/MD.0000000000003291.][This corrects the article DOI: 10.1097/MD.0000000000003731.][This corrects the article DOI: 10.1097/MD.0000000000003791.][This corrects the article DOI: 10.1097/MD.0000000000003805.][This corrects the article DOI: 10.1097/MD.0000000000003827.][This corrects the article DOI: 10.1097/MD.0000000000003863.][This corrects the article DOI: 10.1097/MD.0000000000003878.][This corrects the article DOI: 10.1097/MD.0000000000003879.][This corrects the article DOI: 10.1097/MD.0000000000003884.][This corrects the article DOI: 10.1097/MD.0000000000003900.][This corrects the article DOI: 10.1097/MD.0000000000003513.][This corrects the article DOI: 10.1097/MD.0000000000003631.][This corrects the article DOI: 10.1097/MD.0000000000003644.][This corrects the article DOI: 10.1097/MD.0000000000003692.][This corrects the article DOI: 10.1097/MD.0000000000003701.][This corrects the article DOI: 10.1097/MD.0000000000003706.][This corrects the article DOI: 10.1097/MD.0000000000003712.][This corrects the article DOI: 10.1097/MD.0000000000003767.][This corrects the article DOI: 10.1097/MD.0000000000003781.][This corrects the article DOI: 10.1097/MD.0000000000003808.][This corrects the article DOI: 10.1097/MD.0000000000003831.][This corrects the article DOI: 10.1097/MD.0000000000003837.][This corrects the article DOI: 10.1097/MD.0000000000003839.][This corrects the article DOI: 10.1097/MD.0000000000003842.][This corrects the article DOI: 10.1097/MD.0000000000003843.][This corrects the article DOI: 10.1097/MD.0000000000003847.][This corrects the article DOI: 10.1097/MD.0000000000003848.][This corrects the article DOI: 10.1097/MD.0000000000003850.][This corrects the article DOI: 10.1097/MD.0000000000003861.][This corrects the article DOI: 10.1097/MD.0000000000003862.][This corrects the article DOI: 10.1097/MD.0000000000003864.][This corrects the article DOI: 10.1097/MD.0000000000003866.][This corrects the article DOI: 10.1097/MD.0000000000003871.][This corrects the article DOI: 10.1097/MD.0000000000003872.][This corrects the article DOI: 10.1097/MD.0000000000003880.][This corrects the article DOI: 10.1097/MD.0000000000003878.][This corrects the article DOI: 10.1097/MD.0000000000003873.][This corrects the article DOI: 10.1097/MD.0000000000003876.][This corrects the article DOI: 10.1097/MD.0000000000003879.][This corrects the article DOI: 10.1097/MD.0000000000003881.][This corrects the article DOI: 10.1097/MD.0000000000003884.][This corrects the article DOI: 10.1097/MD.0000000000003885.][This corrects the article DOI: 10.1097/MD.0000000000003888.][This corrects the article DOI: 10.1097/MD.0000000000003889.][This corrects the article DOI: 10.1097/MD.0000000000003891.][This corrects the article DOI: 10.1097/MD.0000000000003893.][This corrects the article DOI: 10.1097/MD.0000000000003894.][This corrects the article DOI: 10.1097/MD.0000000000003897.][This corrects the article DOI: 10.1097/MD.0000000000003899.][This corrects the article DOI: 10.1097/MD.0000000000003900.][This corrects the article DOI: 10.1097/MD.0000000000003901.][This corrects the article DOI: 10.1097/MD.0000000000003902.][This corrects the article DOI: 10.1097/MD.0000000000003903.][This corrects the article DOI: 10.1097/MD.0000000000003904.][This corrects the article DOI: 10.1097/MD.0000000000003908.][This corrects the article DOI: 10.1097/MD.0000000000003910.][This corrects the article DOI: 10.1097/MD.0000000000003912.][This corrects the article DOI: 10.1097/MD.0000000000003916.][This corrects the article DOI: 10.1097/MD.0000000000003917.][This corrects the article DOI: 10.1097/MD.0000000000003918.][This corrects the article DOI: 10.1097/MD.0000000000003920.][This corrects the article DOI: 10.1097/MD.0000000000003921.][This corrects the article DOI: 10.1097/MD.0000000000003923.][This corrects the article DOI: 10.1097/MD.0000000000003924.][This corrects the article DOI: 10.1097/MD.0000000000003925.][This corrects the article DOI: 10.1097/MD.0000000000003934.][This corrects the article DOI: 10.1097/MD.0000000000003941.][This corrects the article DOI: 10.1097/MD.0000000000003944.][This corrects the article DOI: 10.1097/MD.0000000000003970.].
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