|
|
ORIGINAL ARTICLE |
|
Year : 2016 | Volume
: 7
| Issue : 1 | Page : 82 |
|
Abdominal obesity indicators: Waist circumference or waist-to-hip ratio in Malaysian adults population
Norfazilah Ahmad1, Samia Ibrahim Mohamed Adam1, Azmawati Mohammed Nawi1, Mohd Rohaizat Hassan1, Hasanain Faisal Ghazi2
1 Department of Community Health, Faculty of Medicine, Universiti Kebangsaan Malaysia Medical Centre, Kuala Lumpur, Malaysia 2 Department of Community Medicine, International Medical School, Management and Science University, Selangor, Malaysia
Date of Submission | 10-Nov-2015 |
Date of Acceptance | 05-May-2016 |
Date of Web Publication | 08-Jun-2016 |
Correspondence Address: Mohd Rohaizat Hassan Department of Community Health, Faculty of Medicine, Universiti Kebangsaan Malaysia Medical Centre, Jalan Yaacob Latif, Bandar Tun Razak, Cheras 56000 Kuala Lumpur Malaysia
 Source of Support: None, Conflict of Interest: None  | Check |
DOI: 10.4103/2008-7802.183654
Background: Waist circumference (WC) is an accurate and simple measure of abdominal obesity as compared to waist-hip ratio (WHR). The aim of this study was to determine the correlation between body mass index (BMI) with WC and WHR and suggest cutoff points for WC among Rural Malaysian adults. Methods: A cross-sectional study was conducted among 669 respondents from three villages in Tanjung Karang, located in the district of Kuala Selangor. Data collection was carried out by guided questionnaires and anthropometric measures. Results: The prevalence of abdominal obesity for BMI was almost similar for both gender across Caucasian and Asian BMI cutoff points. Based on Caucasian cutoff points, the prevalence of abdominal obesity for WC was 23.8% (male) and 66.4% (female) while for WHR was 6.2% (male) and 54.2% (female). Asian cutoff points gave higher prevalence of abdominal obesity compared to that of WC among male respondents and WHR for both genders. WC showed strong and positive correlation with BMI compared to WHR (in male WC r = 0.78, WHR r = 0.24 and in female WC r = 0.72, WHR r = 0.19; P < 0.001). Receiver operating characteristic curve analysis suggested WC cutoff points of 92.5 cm in men and 85.5 cm in women is the optimal number for detection of abdominal obesity. Conclusions: WC is the best indicator as compared with WHR for abdominal obesity for Malaysian adults. Keywords: Abdominal obesity, adults, body mass index, waist circumference, waist-to-hip ratio
How to cite this article: Ahmad N, Adam SI, Nawi AM, Hassan MR, Ghazi HF. Abdominal obesity indicators: Waist circumference or waist-to-hip ratio in Malaysian adults population. Int J Prev Med 2016;7:82 |
How to cite this URL: Ahmad N, Adam SI, Nawi AM, Hassan MR, Ghazi HF. Abdominal obesity indicators: Waist circumference or waist-to-hip ratio in Malaysian adults population. Int J Prev Med [serial online] 2016 [cited 2023 Jun 2];7:82. Available from: https://www.ijpvmjournal.net/text.asp?2016/7/1/82/183654 |
Introduction | |  |
There are various terms of obesity loosely used such as abdominal obesity, abdominal adiposity, body fat percentage, and predictors for obesity. Body mass index (BMI) is the most commonly used parameter to measure abdominal obesity for determining whether someone may be defined as obese, overweight, or normal weight. BMI is the person's weight in kilograms divided by the square of the height in meters. Many epidemiological studies have demonstrated that different anthropometric measures for abdominal obesity such as BMI, waist circumference (WC), and waist-hip ratio (WHR) are strong and consistent predictors for noncommunicable diseases such as type 2 diabetes mellitus [1] and cardiovascular disease (CVD). [2]
World Health Organization (WHO) guidelines state that alternative measures that reflect abdominal obesity such as WC, WHR, and waist-to-height ratio (WHtR) have been found to be superior to BMI. [3] A study among Chinese population demonstrated that while BMI and WC were found to be the important indices of obesity, WC was found to be the best measurement of obesity whereas WHR could be used as an alternative indicator for obesity. [4] Similarly, WC was also found to be a simple and more accurate predictor of type 2 diabetes mellitus than other indices such as BMI and WHR. [5]
WHR was suggested as better anthropometric measure for estimating the risk of type 2 diabetes mellitus, and the optimal cutoff values of 0.89 for men and 0.82 for women was set for Asian population such as in the Taiwanese population. [6] Furthermore, another study done in Iran by Hajian-Tilaki and Heidari [7] concluded that WC and WHtR exhibited are slightly better performance than BMI for diabetes in both sexes, particularly among women. In addition, hypertensive patients had a significantly higher WHR (>0.9) as well as a significantly higher BMI (>25 kg/m²) compared to the normotensive one. [8]
WC is an important measure of abdominal obesity compared to WHR, which can be low in some obese people because of high hip circumference (in denominator). Sometimes it is difficult in clinical setting to obtain an accurate measurement of hip circumference as compared to WC. In another study, WHR managed to identify more women in the underweight and normal groups as abdominally obese than did WC. A high WHR in a nonobese woman would also suggest that the hip circumference was low. [9] Although many research works have been done worldwide on various aspects of anthropometric measurement to predict the risk of obesity-related diseases, the correlation of BMI with WC and WHR has seldom been addressed among Rural Malaysian population.
The aim of this study was to determine the correlation between BMI with WC and WHR and suggest cutoff points for WC among respondents from three villages in Tanjung Karang, Selangor, Malaysia.
Methods | |  |
Study design and participants
A cross-sectional study was conducted among Malaysian population in three villages in Tanjung Karang, located in the district of Kuala Selangor about 15 km away from the town of Kuala Selangor, Malaysia. Malaysian citizens aged 18 years and above were recruited to participate in the study, which was carried out from June to September 2011. The simple random sampling procedure was used to choose the respondents from the name list of villagers. Those who refuse to participate and pregnant women were excluded. The total sample size of 669 was calculated [10] with mean difference and combine the standard deviation (SD) of WC (n = 360, mean: 89.90 [10.78] for male and WC (n = 412, mean: 88.02 [12.23] for female.
Study tools
Data collection was carried out by guided questionnaires consist of two sections; section A for baseline characteristic and B for anthropometric measurements.
Weight was measured to the nearest 0.1 kg using electronic weight scale (model 770: Seca, Germany) with the respondents lightly clothed height was measured to the nearest 0.5 cm with measuring tape while the respondent stood still without shoes. BMI was calculating by the formula, BMI = weight (kg)/height (m²). The respondents were divided into four categories based on their BMI according to the BMI cutoffs points (Caucasian) as follows, underweight (BMI <18.5), normal (18.5-24.9), preobese or overweight (25-29.9), and obese class (BMI ≥30) (WHO 2008) and BMI cutoff points (Asian) as follows; BMI <18.5 kg/m² (lean or underweight), between 18.5 and 22.9 kg/m² (normal), between 23 and 27.49 kg/m² (overweight) and 27.5 kg/m² or above as (obese). [11]
WC was measured at the end of several consecutive natural breaths, at the level parallel to the floor, midpoint between the top of the iliac crest and the lower margin of the last palpable rib in midaxillary line. [3] The data were analyzed using cutoffs points for Caucasians (94 cm in men and 80 cm in women) and cutoffs points for Asians (90 cm in men and 80 cm in women). [3],[12]
WHR was calculated by dividing WC (in cm) by hip circumference (cm). Hip circumference was measured at a level parallel to floor, at the largest circumference of the buttocks. The cutoffs points (Caucasian) of WHR used (>1 in men and >0.85 in women) [13] and cutoffs points for Asians used (0.95 in men and 0.80 in women) [14] denote abdominal obesity. Average of two readings was used for analysis.
Research ethics
The study was approved by the Medical Faculty, Universiti Kebangsaan Malaysia. Permission to enter the villages was obtained from the head villages and the respondents provided with informed consent. All co-researchers were briefed and trained before data collection.
Statistical analysis
Data were analyzed using SPSS version 19 (IBM SPSS Statistics for Windows, Armonk, NY: IBM Corp) for descriptive and correlation analysis. Descriptive analysis used for continuous variables was mean and SD while categorical data were presented as frequency and percentage. Pearson's correlation (r) was conducted to determine the correlation of BMI with WC and WHR. Receiver operating characteristic (ROC) curve was used to show if the optimal cutoff points of WC for this particular population is similar or different with the standard cutoff points for Caucasian. Statistical significance was set at P < 0.05.
Results | |  |
[Table 1] shows that out of 669 respondents, 273 (40.8%) were male and 396 (59.2%) were female. Female had higher mean of BMI compared to male respondents while males had higher mean of WC and WHR compared to female respondents. BMI cutoff points for Caucasian identified lower total prevalence of overweight and obese compared to cutoff points for Asian (44.2% and 60.0%, respectively) [Table 2]. Among the male respondents, Caucasian BMI cutoff points showed lower prevalence of overweight and obese respondents compared to Asian cutoff points. Nevertheless, Caucasian BMI cutoff points showed higher prevalence of overweight respondents compared to Asian cutoff points among the female respondents. | Table 1: Abdominal obesity based on 3 indicators among Malaysian adults (2011) (n=669)
Click here to view |
 | Table 2: Prevalence of abdominal obesity across body mass index class based on Caucasian and Asian cutoff points among Malaysian adults (2011) (n=667)
Click here to view |
[Table 3] indicates that, based on Caucasian cutoff points for WC and WHR, the male respondents had the prevalence of abdominal obesity of 23.8% and 6.2%, respectively. Whereas. Asian cutoff points for WC and WHR identified higher prevalence of abdominal obesity among the male respondents (37.4% and 26.4%, respectively). Both cutoff points identified similar prevalence of abdominal obesity for WC among the female respondents. However, the Asian cutoff points for WHR showed a higher prevalence of abdominal obesity among female respondents. | Table 3: Prevalence of abdominal obesity according to waist circumference and waist-hip ratio across gender among Malaysian adults (2011) (n=669)
Click here to view |
[Table 4] illustrates the Pearson correlation between BMI with WC and WHR. WC was moderately strong and positive correlated with BMI (r = 0.727, P < 0.001) while WHR was poorly and positive correlated with BMI (r = 0.176, P < 0.001). Results also indicated that WC was better correlated with BMI as compared to WHR for both male and female. [Figure 1] and [Figure 2] indicate the ROC curve for both male and female, the cutoff point of WC among male at optimal sensitivity (87.9%) and specificity (82.9%) was 92.5 cm (area under the curve [AUC] = 0.93; 95% confidence interval [CI] = 0.87, 0.99), which is near to Caucasian cutoff point, and among female the cutoff point at optimal sensitivity (94.0%) and specificity (55.4%) was 85.5 cm (AUC = 0.87, 95% CI = 0.83,0.91), which is higher than the Caucasian and Asian cutoff points. | Figure 1: Receiver operating characteristic for waist circumference among male respondents
Click here to view |
 | Figure 2: Receiver operating characteristic for waist circumference among female respondents
Click here to view |
 | Table 4: Pearson correlation coefficients between body mass index with waist circumference and waist-hip ratio across gender among Malaysian adults (2011)
Click here to view |
Discussion | |  |
This study showed that the BMI cutoff points for Asian gave higher prevalence of overweight and obese among the respondents compared to BMI cutoff points for Caucasian. However, WC cutoff points for Asian gave a higher prevalence of abdominal obesity among male respondents compared to WC cutoff points for Caucasian. This finding supported the WHO monograph on obesity which recommended even lower cutoff for BMI and WC for Asians. [11]
Studies conducted locally using BMI and WC cutoff points for Caucasian showed that prevalence of overweight and obesity among Malaysian adults using BMI were 29.1% and 14.2% for males and females respectively in the Third National Health and Morbidity Survey in 2006. [15] Meanwhile overall national prevalence of abdominal obesity using WC was 17.4% among adult Malaysian with women at higher risk (26.0%) compared to men (7.2%). [16] A study among Indian found that the prevalence of abdominal obesity using WC were 46% in men and 64% in women and using WHR were about 12% in men and 68% in women (using the lowest cutoff points recommended for Asians). [9]
In general, the result of this study showed that WC has a strong positive correlation with BMI as compared to WHR, which is congruent with findings from other studies. BMI and WC are more useful than WHR for predicting two or more nonadipose components of metabolic syndrome [17] and WC was a stronger indicator of the risk of diabetes than BMI. [18] BMI and WC showed strong positive correlation (r = 0.68-0.73; P < 0.0001) with body fat percentage in both sexes, but the correlation was weaker for WHR (r = 0.30-0.41; P < 0.0001). [19] WC was also found to correlate positively and significantly with BMI compared to WHR in diabetic females and males. [20]
ROC analysis in our study indicated that WC had the best sum of sensitivity and specificity compared to WHR. This is consistent with a study among Malaysian population which found WC is the better indicator for predicting CVD risk factors than BMI and the optimal cutoffs for WC were 83-92 cm in men and 83-88 cm in women. [21] A study among Chinese population stated that WC is the best predictor of hyperglycemia and the optimal cutoffs for WC was 85 for men 82 for women. [22]
In our study, the prevalence of abdominal obesity using WC was higher than using WHR. The WHR for both standard and Asian cutoffs detect more underweight and normal respondents as being abdominal obese may be attributed to the small hip circumference (in dominator) leading to high WHR. This is consistent with the finding among the Indian population. [9] The limitations of our study were due to primarily the study among population not differentiated by socio-demographic characteristics such as age or ethnicity. Secondly, due to multiple missing data which prevent further analysis. However, the large sample size in this study make our results useful as a baseline data for future research, especially focusing on waist circumference as a screening tool for abdominal obesity.
Conclusions | |  |
The WC shows strong and positive correlation with BMI as compared to WHR. Using both Caucasian and Asian cutoff points, the prevalence of abdominal obesity using WC were found to be higher than in using WHR, as the WHR for both Caucasian and Asian cutoffs detect more underweight and normal respondents as being abdominally obese. The optimal cutoff points of WC for detecting abdominal obesity in this adult Malaysian population were somewhere in between the Caucasian and Asian cutoff points 92.5 for males. The optimal cutoff points for WC among female was found to be higher than both Caucasian and Asian cutoff points.
Acknowledgements
We would like to thank the village heads and respondents of Kampung Api-Api and Kampung Kuantan, Kuala Selangor, Selangor for their kind and unlimited cooperation and support. We also would like to acknowledge the 3 rd Year Medical Students (2011/2012) of Faculty of Medicine, UniversitiKebangsaan Malaysia Medical Centre for their involvement during the initial data collection of the study.
Financial support and sponsorship
Nil.
Conflicts of interest
There are no conflicts of interest.
References | |  |
1. | Vazquez G, Duval S, Jacobs DR Jr., Silventoinen K. Comparison of body mass index, waist circumference, and waist/hip ratio in predicting incident diabetes: A meta-analysis. Epidemiol Rev 2007;29:115-28. |
2. | Paniagua L, Lohsoonthorn V, Lertmaharit, Jiamjarasrangsi W, Williams MA. Comparison of waist circumference, body mass index, percent body fat and other measure of adiposity in identifying cardiovascular disease risks among Thai adults. Obes Res Clin Pract 2008;2:215-23. |
3. | World Health Organization (WHO). Waist Circumference and Waist-Hip Ratio. Report of WHO Expert Consultation. Geneva: World Health Organization; 2008. |
4. | Yanga F, Lv JH, Lei SF, Chena XD. Receiver-operating characteristic analyses of body mass index, waist circumference and waist-to-hip ratio for obesity: Screening in young adults in central south of China. Clin Nut 2006;25:1030-9. |
5. | Mamtani MR, Kulkarni HR. Predictive performance of anthropometric indexes of central obesity for the risk of type 2 diabetes. Arch Med Res 2005;36:581-9. |
6. | Cheng CH, Ho CC, Yang CF, Huang YC, Lai CH, Liaw YP. Waist-to-hip ratio is a better anthropometric index than body mass index for predicting the risk of type 2 diabetes in Taiwanese population. Nutr Res 2010;30:585-93. |
7. | Hajian-Tilaki K, Heidari B. Is waist circumference a better predictor of diabetes than body mass index or waist-to-height ratio in Iranian adults? Int J Prev Med 2015;6:5.  [ PUBMED] |
8. | Sanya AO, Ogwum Ike OO, Ige AP, Ayanniyi OA. Relationship of waist-hip ratio and body mass index to blood pressure of individuals in Ibadan North Local Government. AJPARS 2009;1:7-11. |
9. | Kurpad SS, Tandon H, Srinivasan K. Waist circumference correlates better with body mass index than waist-to-hip ratio in Asian Indians. Natl Med J India; 16:189-92. |
10. | Snedecor GW, Cochran WG. Statistical Methods. 8 th ed. Ames: Iowa State Press; 1989. |
11. | WHO Expert Consultation. Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet 2004;363:157-63. |
12. | Zimmet PZ, Alberti KG. Introduction: Globalization and the non-communicable disease epidemic. Obesity (Silver Spring) 2006;14:1-3. |
13. | World Health Organization (WHO). The Problem of Obesity; 2000. Available from: http://www.whqlibdoc.who.int/trs/WHO_TRS_894_(part1).pdf. [Last accessed on 2015 Aug 15]. |
14. | Lean ME, Han TS, Morrison CE. Waist circumference as a measure for indicating need for weight management. BMJ 1995;311:158-61. |
15. | Ministry of Health (MOH). The Third National health and Morbidity Survey (NHMS III). Vol. 1. Malaysia: Ministry of Health (MOH); 2006. |
16. | Kee CC Jr., Jamaiyah H, Noor Safiza MN, Khor GL, Suzana S, Jamalludin AR, et al. Abdominal obesity in Malaysian adults: National health and morbidity survey III (NHMS III, 2006). Malays J Nutr 2008;14:125-35. |
17. | Wang F, Wu S, Song Y, Tang X, Marshall R, Liang M, et al. Waist circumference, body mass index and waist to hip ratio for prediction of the metabolic syndrome in Chinese. Nutr Metab Cardiovasc Dis 2009;19:542-7. |
18. | Humayun A, Shah AS. Comparison of body mass index and waist circumference in predicting incident diabetes. Pak J Physiol 2010;6:47-9. |
19. | Neovius M, Linné Y, Rossner S. BMI, waist-circumference and waist-hip-ratio as diagnostic tests for fatness in adolescents. Int J Obes (Lond) 2005;29:163-9. |
20. | Marjani A. Waist circumference, body mass index, hip circumference and waist-to-hip ratio in type 2 diabetes patients in Gorgan, Iran. J Clin diag Res 2011;5:201-5. |
21. | Zaher ZM, Zambari R, Pheng CS, Muruga V, Ng B, Appannah G, et al. Optimal cut-off levels to define obesity: Body mass index and waist circumference, and their relationship to cardiovascular disease, dyslipidaemia, hypertension and diabetes in Malaysia. Asia Pac J Clin Nutr 2009;18:209-16. |
22. | Xu F, Wang YF, Lu L, Liang Y, Wang Z, Hong X, et al. Comparison of anthropometric indices of obesity in predicting subsequent risk of hyperglycemia among Chinese men and women in Mainland China. Asia Pac J Clin Nutr 2010;19:586-93. |
[Figure 1], [Figure 2]
[Table 1], [Table 2], [Table 3], [Table 4]
This article has been cited by | 1 |
Utility of Indian diabetes risk score for the screening of type 2 diabetes mellitus and cardiovascular disease in and around areas of Lucknow |
|
| Mohd Danish Khan, Mohammad Kaleem Ahmad, Roshan Alam, Geeta Jaiswal, Mohammad Mustufa Khan | | International Journal of Diabetes in Developing Countries. 2023; | | [Pubmed] | [DOI] | | 2 |
Abdominal obesity in India: analysis of the National Family Health Survey-5 (2019–2021) data |
|
| Monika Chaudhary, Priyanshu Sharma | | The Lancet Regional Health - Southeast Asia. 2023; 14: 100208 | | [Pubmed] | [DOI] | | 3 |
Analysis of circulating extracellular vesicle derived microRNAs in breast cancer patients with obesity: a potential role for Let-7a |
|
| Ines Barone, Luca Gelsomino, Felice Maria Accattatis, Francesca Giordano, Balazs Gyorffy, Salvatore Panza, Mario Giuliano, Bianca Maria Veneziani, Grazia Arpino, Carmine De Angelis, Pietro De Placido, Daniela Bonofiglio, Sebastiano Andò, Cinzia Giordano, Stefania Catalano | | Journal of Translational Medicine. 2023; 21(1) | | [Pubmed] | [DOI] | | 4 |
Knowledge, awareness, and presence of cardiovascular risk factors among college staff of a Nigerian University |
|
| Uchechukwu Martha Chukwuemeka, Favour Chidera Okoro, Uchenna Prosper Okonkwo, Ifeoma Adaigwe Amaechi, Anthony Chinedu Anakor, Ifeoma Uchenna Onwuakagba, Christiana Nkiru Okafor | | Bulletin of Faculty of Physical Therapy. 2023; 28(1) | | [Pubmed] | [DOI] | | 5 |
Simple anthropometric measures to predict visceral adipose tissue area in middle-aged Indonesian men |
|
| Sahat Basana Romanti Ezer Matondang, Bennadi Adiandrian, Komang Shary Karismaputri, Cicilia Marcella, Joedo Prihartono, Dicky Levenus Tahapary, Yosuke Yamada | | PLOS ONE. 2023; 18(1): e0280033 | | [Pubmed] | [DOI] | | 6 |
Relationship of the Body Mass Index and waist circumference with the severity of heart failure among patients in Makassar, South Sulawesi, Indonesia |
|
| Desvita G. AMIRUDDIN, Pendrik TANDEAN, Andi M. AMAN, Haerani RASYID, Syakib BAKRI, Tutik HARJIANTI, Faridin H. PANGO, Arifin SEWENG | | Gazzetta Medica Italiana Archivio per le Scienze Mediche. 2023; 182(3) | | [Pubmed] | [DOI] | | 7 |
Modifiable determinants of central obesity among the rural black population in the DIMAMO HDSS, Limpopo, South Africa |
|
| Cairo B. Ntimana, Solomon S. R. Choma | | Frontiers in Public Health. 2023; 11 | | [Pubmed] | [DOI] | | 8 |
Gender and Age Differences in Anthropometric Characteristics of Taiwanese Older Adults Aged 65 Years and Older |
|
| Yan-Jhu Su, Chien-Chang Ho, Po-Fu Lee, Chi-Fang Lin, Yi-Chuan Hung, Pin-Chun Chen, Chang-Tsen Hung, Yun-Chi Chang | | Healthcare. 2023; 11(9): 1237 | | [Pubmed] | [DOI] | | 9 |
The Correlation between Waist Circumference and the Pro-Inflammatory Adipokines in Diabetic Retinopathy of Type 2 Diabetes Patients |
|
| Yeo Jin Lee, Joeng Ju Kim, Jongmin Kim, Dong-Woo Cho, Jae Yon Won | | International Journal of Molecular Sciences. 2023; 24(3): 2036 | | [Pubmed] | [DOI] | | 10 |
Taste Function in Adult Humans from Lean Condition to Stage II Obesity: Interactions with Biochemical Regulators, Dietary Habits, and Clinical Aspects |
|
| Alessandro Micarelli, Alessandra Vezzoli, Sandro Malacrida, Beatrice Micarelli, Ilaria Misici, Valentina Carbini, Ilaria Iennaco, Sara Caputo, Simona Mrakic-Sposta, Marco Alessandrini | | Nutrients. 2023; 15(5): 1114 | | [Pubmed] | [DOI] | | 11 |
Predictors of Coronary Heart Disease (CHD) among Malaysian Adults: Findings from MyDiet-CHD Study |
|
| Wan Zulaika Wan Musa, Aryati Ahmad, Nur Ain Fatinah Abu Bakar, Nadiah Wan- Arfah, Ahmad Wazi Ramli, Nyi Nyi Naing | | Malaysian Journal of Medicine and Health Sciences. 2022; 18(6): 259 | | [Pubmed] | [DOI] | | 12 |
A Review on CRP Analysis and Obesity Influence in the Disparity of COVID-19 Pandemic |
|
| Moussa Mohammed Elamin | | Pharmacophore. 2022; 13(1): 48 | | [Pubmed] | [DOI] | | 13 |
Association of Dietary Pattern with Cardiovascular Risk Factors among Postmenopausal Women in Taiwan: A Cross-Sectional Study from 2001 to 2015 |
|
| Sabrina Aliné, Chien-Yeh Hsu, Hsiu-An Lee, Rathi Paramastri, Jane C.-J. Chao | | Nutrients. 2022; 14(14): 2911 | | [Pubmed] | [DOI] | | 14 |
The Impact of Obesity on Cardiovascular Fitness in Young Individuals |
|
| Prajakta Radke, Sheela Bargal, Swati Sonawane | | Cureus. 2022; | | [Pubmed] | [DOI] | | 15 |
Comparison of the Ability of Anthropometric Indices to Predict the Risk of Diabetes Mellitus in South African Males: SANHANES-1 |
|
| Machoene D. Sekgala, Ronel Sewpaul, Maretha Opperman, Zandile J. Mchiza | | International Journal of Environmental Research and Public Health. 2022; 19(6): 3224 | | [Pubmed] | [DOI] | | 16 |
Relationship between the Dietary Inflammatory Index and Cardiovascular Health among Children |
|
| Ana Isabel Mora-Urda, Francisco Javier Martín-Almena, María del Pilar Montero López | | International Journal of Environmental Research and Public Health. 2022; 19(23): 15706 | | [Pubmed] | [DOI] | | 17 |
Causality of anthropometric markers associated with polycystic ovarian syndrome: Findings of a Mendelian randomization study |
|
| Kushan De Silva, Ryan T. Demmer, Daniel Jönsson, Aya Mousa, Helena Teede, Andrew Forbes, Joanne Enticott, Renato Polimanti | | PLOS ONE. 2022; 17(6): e0269191 | | [Pubmed] | [DOI] | | 18 |
Associations between health-related quality of life and measures of adiposity among Filipino adults |
|
| Joseph Capuno, Aleli Kraft, Kayleen Gene Calicdan, Owen O’Donnell, Alvaro Reischak-Oliveira | | PLOS ONE. 2022; 17(10): e0275798 | | [Pubmed] | [DOI] | | 19 |
Pengukuran Status Gizi dan Pengobatan Penyakit Metabolik Warga Kelurahan Angke, Jakarta Barat |
|
| Yohana Yohana, Meiyanti Meiyanti, Erlani Kartadinata, Eveline Margo | | Jurnal ABDINUS : Jurnal Pengabdian Nusantara. 2022; 6(2): 305 | | [Pubmed] | [DOI] | | 20 |
The most effective exercise to prevent obesity: A longitudinal study of 33,731 Taiwan biobank participants |
|
| Wan-Yu Lin | | Frontiers in Nutrition. 2022; 9 | | [Pubmed] | [DOI] | | 21 |
Substantial but Misunderstood Human Sexual Dimorphism Results Mainly From Sexual Selection on Males and Natural Selection on Females |
|
| William D. Lassek, Steven J. C. Gaulin | | Frontiers in Psychology. 2022; 13 | | [Pubmed] | [DOI] | | 22 |
Obesity-related vascular dysfunction persists after weight loss and is associated with decreased vascular glucagon-like peptide (GLP-1) receptor in female rats |
|
| Risa Kiernan, Dhandevi Persand, Nicole Maddie, Weikang Cai, Maria Alicia Carrillo-Sepulveda | | American Journal of Physiology-Heart and Circulatory Physiology. 2022; | | [Pubmed] | [DOI] | | 23 |
Modifiable risk factors in adults with and without prior cardiovascular disease: findings from the Indonesian National Basic Health Research |
|
| Dian Sidik Arsyad, Jan Westerink, Maarten J. Cramer, Jumriani Ansar, Wahiduddin, Frank L. J. Visseren, Pieter A. Doevendans, Ansariadi | | BMC Public Health. 2022; 22(1) | | [Pubmed] | [DOI] | | 24 |
Genomic correlation, shared loci, and causal relationship between obesity and polycystic ovary syndrome: a large-scale genome-wide cross-trait analysis |
|
| Qianwen Liu, Zhaozhong Zhu, Peter Kraft, Qiaolin Deng, Elisabet Stener-Victorin, Xia Jiang | | BMC Medicine. 2022; 20(1) | | [Pubmed] | [DOI] | | 25 |
Can the relationship between overweight/obesity and sleep quality be explained by affect and behaviour? |
|
| S. W. Eid, R. F. Brown, S. K. Maloney, C. L. Birmingham | | Eating and Weight Disorders - Studies on Anorexia, Bulimia and Obesity. 2022; | | [Pubmed] | [DOI] | | 26 |
General and abdominal obesity operate differently as influencing factors of fracture risk in old adults |
|
| Xiao-Wei Zhu, Ke-Qi Liu, Cheng-Da Yuan, Jiang-Wei Xia, Yu Qian, Lin Xu, Jian-Hua Gao, Xiao-Li Rong, Guo-Bo Chen, David Karasik, Shu-Yang Xie, Hou-Feng Zheng | | iScience. 2022; 25(6): 104466 | | [Pubmed] | [DOI] | | 27 |
Smell Impairment in Stage I-II Obesity: Correlation with Biochemical Regulators and Clinical Aspects |
|
| Alessandro Micarelli, Simona Mrakic-Sposta, Beatrice Micarelli, Sandro Malacrida, Ilaria Misici, Valentina Carbini, Ilaria Iennaco, Sara Caputo, Alessandra Vezzoli, Marco Alessandrini | | The Laryngoscope. 2022; | | [Pubmed] | [DOI] | | 28 |
Association of chronotype with eating habits and anthropometric measures in a sample of Iranian adults |
|
| Sheida Zeraattalab-Motlagh, Azadeh Lesani, Maryam Majdi, Sakineh Shab-Bidar | | British Journal of Nutrition. 2022; : 1 | | [Pubmed] | [DOI] | | 29 |
Association of Anthropometric and Body Adiposity Measures with Thyroid Dysfunction in Clinical Settings of Manipur, Northeast India |
|
| Kh Dimkhohoi Baite, Ajit Lukram, Jamkhoupum Baite, Sanjenbam Yaiphaba Meitei | | Journal of Health and Allied Sciences NU. 2022; | | [Pubmed] | [DOI] | | 30 |
Comparing the performance of body mass index, waist circumference and waist-to-height ratio in predicting Malaysians with excess adiposity |
|
| Nie Yen Low, Chin Yi Chan, Shaanthana Subramaniam, Kok-Yong Chin, Soelaiman Ima Nirwana, Norliza Muhammad, Ahmad Fairus, Pei Yuen Ng, Nor Aini Jamil, Noorazah Abd Aziz, Norazlina Mohamed | | Annals of Human Biology. 2022; : 1 | | [Pubmed] | [DOI] | | 31 |
Relationship Between Dairy Consumption and Abdominal Obesity |
|
| Yagmur Yasar Firat,Neriman Inanc,Meltem Soylu,Eda Basmisirli,Asli Gizem Capar,Yusuf Aykemat | | Journal of the American College of Nutrition. 2021; : 1 | | [Pubmed] | [DOI] | | 32 |
Frequency distribution and association of Fat-mass and obesity (FTO) gene SNP rs-9939609 variant with Diabetes Mellitus Type-II population of Hyderabad, Sindh, Pakistan |
|
| Farheen Shaikh,Tazeen shah,Norah Abdullah Bazekh Madkhali,Ahmed Gaber,Walaa F. Alsanie,Sanum Ali,Shafaq Ansari,Muhammad Rafiq,R.Z. Sayyed,Nadir Ali Rind,Khalid Hussain Rind,Akhtar Hussain Shar,Syed Mohammed Basheeruddin Asdaq | | Saudi Journal of Biological Sciences. 2021; | | [Pubmed] | [DOI] | | 33 |
The effect of metformin on body mass index and metabolic parameters in non-diabetic HIV-positive patients: a meta-analysis |
|
| Narges Nazari Harmooshi,Ahmad abeshtan,Mehrnoush Zakerkish,Golshan Mirmomeni,Fakher Rahim | | Journal of Diabetes & Metabolic Disorders. 2021; | | [Pubmed] | [DOI] | | 34 |
Obesity and endocrine therapy resistance in breast cancer: Mechanistic insights and perspectives |
|
| Ines Barone,Amanda Caruso,Luca Gelsomino,Cinzia Giordano,Daniela Bonofiglio,Stefania Catalano,Sebastiano Andò | | Obesity Reviews. 2021; | | [Pubmed] | [DOI] | | 35 |
Association of anthropometric indices of obesity with hypertension among public employees in northern Ethiopia: findings from a cross-sectional survey |
|
| KM Saif-Ur-Rahman,Chifa Chiang,Lemlem Weldegerima Gebremariam,Esayas Haregot Hilawe,Yoshihisa Hirakawa,Atsuko Aoyama,Hiroshi Yatsuya | | BMJ Open. 2021; 11(9): e050969 | | [Pubmed] | [DOI] | | 36 |
Awareness, treatment, control, and determinants of dyslipidemia among adults in China |
|
| Sampson Opoku,Yong Gan,Emmanuel Addo Yobo,David Tenkorang-Twum,Wei Yue,Zhihong Wang,Zuxun Lu | | Scientific Reports. 2021; 11(1) | | [Pubmed] | [DOI] | | 37 |
The association between dietary inflammatory index with sleep quality and obesity amongst iranian female students: A cross-sectional study |
|
| Hadi Bazyar,Ahmad Zare Javid,Hossein Bavi Behbahani,Nitin Shivappa,James R. Hebert,Sara Khodaramhpour,Sara Khaje Zadeh,Vahideh Aghamohammadi | | International Journal of Clinical Practice. 2021; | | [Pubmed] | [DOI] | | 38 |
Anthropometric indices obesity and cardiometabolic risk: is there a link? |
|
| A. V. Svarovskaya,A. A. Garganeeva | | Cardiovascular Therapy and Prevention. 2021; 20(4): 2746 | | [Pubmed] | [DOI] | | 39 |
Changes in lipid profile and some biochemical parameters in postmenopausal women treated with honey |
|
| E C Ogbodo,B N Ugorji,S C Meludu,I S I Ogbu,L O Egejuru,D O Chikezie,C F Igwebuobi | | Journal of Preventive Medicine and Holistic Health. 2021; 7(1): 31 | | [Pubmed] | [DOI] | | 40 |
Relationship between anthropometric characteristics and aerobic fitness among Malaysian men and women |
|
| Syazni Razak,Maria Justine,Vikram Mohan | | Journal of Exercise Rehabilitation. 2021; 17(1): 52 | | [Pubmed] | [DOI] | | 41 |
Nordic Walking at Maximal Fat Oxidation Intensity Decreases Circulating Asprosin and Visceral Obesity in Women With Metabolic Disorders |
|
| Malgorzata Kantorowicz,Jadwiga Szymura,Zbigniew Szygula,Justyna Kusmierczyk,Marcin Maciejczyk,Magdalena Wiecek | | Frontiers in Physiology. 2021; 12 | | [Pubmed] | [DOI] | | 42 |
Significance of abdominal obesity and endothelial dysfunction marker in patients undergoing elective coronary stenting |
|
| A. V. Svarovskaya,E. A. Kuzheleva,O. N. Ogurkova,A. A. Garganeeva | | The Siberian Journal of Clinical and Experimental Medicine. 2021; 36(3): 97 | | [Pubmed] | [DOI] | | 43 |
Relationship between Physical Activity and Cardiovascular Risk Factors: A Cross-Sectional Study among Low-Income Housewives in Kuala Lumpur |
|
| Nur Zakiah Mohd Saat,Siti Aishah Hanawi,Nor M. F. Farah,Hazilah Mohd Amin,Hazlenah Hanafiah,Nur Shazana Shamsulkamar | | International Journal of Environmental Research and Public Health. 2021; 18(11): 6090 | | [Pubmed] | [DOI] | | 44 |
Socio-Demographic and General Health Factors Associated with Quality of Life in Long-Term Breast Cancer Survivors from Southwestern Poland |
|
| Malgorzata Socha,Krzysztof A. Sobiech | | International Journal of Environmental Research and Public Health. 2021; 18(17): 9321 | | [Pubmed] | [DOI] | | 45 |
Development of Nonlaboratory-Based Risk Prediction Models for Cardiovascular Diseases Using Conventional and Machine Learning Approaches |
|
| Mirza Rizwan Sajid, Bader A. Almehmadi, Waqas Sami, Mansour K. Alzahrani, Noryanti Muhammad, Christophe Chesneau, Asif Hanif, Arshad Ali Khan, Ahmad Shahbaz | | International Journal of Environmental Research and Public Health. 2021; 18(23): 12586 | | [Pubmed] | [DOI] | | 46 |
Evaluation of Anthropometric Indices and Biochemical Markers in Iranian Prediabetics in Hoveyzeh Located in Southwest Iran: A Cross-sectional Study |
|
| Majed Meripour,Hashem Mohamadian,Morteza Abdullatif Khafaie | | Jundishapur Journal of Chronic Disease Care. 2021; In Press(In Press) | | [Pubmed] | [DOI] | | 47 |
Associations of Physical Activity, Sleep Quality and Cardiovascular Risk Factors in University Students |
|
| N. Z. M. Saat, Siti Aishah Hanawi, Nor M. F. Farah, Hazilah Mohd Amin, Hazlenah Hanafiah, Thavamalar Selvaraj | | Sustainability. 2021; 13(21): 11806 | | [Pubmed] | [DOI] | | 48 |
Impact of Obesity and Being Overweight on the Immunogenicity to Live Attenuated Hepatitis A Vaccine in Children and Young Adults |
|
| Termpong Dumrisilp,Jongkonnee Wongpiyabovorn,Supranee Buranapraditkun,Chomchanat Tubjaroen,Nataruks Chaijitraruch,Sittichoke Prachuapthunyachart,Palittiya Sintusek,Voranush Chongsrisawat | | Vaccines. 2021; 9(2): 130 | | [Pubmed] | [DOI] | | 49 |
What are the optimal cut-off points of anthropometric indices for prediction of overweight and obesity? Predictive validity of waist circumference, waist-to-hip and waist-to-height ratios |
|
| Helda Tutunchi,Mehrangiz Ebrahimi-Mameghani,Alireza Ostadrahimi,Mohammad Asghari-Jafarabadi | | Health Promotion Perspectives. 2020; 10(2): 142 | | [Pubmed] | [DOI] | | 50 |
The Association between Physical Fitness Performance and Abdominal Obesity Risk among Taiwanese Adults: A Cross-Sectional Study |
|
| Po-Fu Lee,Chien-Chang Ho,Nai-Wen Kan,Ding-Peng Yeh,Yun-Chi Chang,Yu-Jui Li,Ching-Yu Tseng,Xin-Yu Hsieh,Chih-Hui Chiu | | International Journal of Environmental Research and Public Health. 2020; 17(5): 1722 | | [Pubmed] | [DOI] | | 51 |
Optimal Body Fat Percentage Cut-Off Values in Predicting the Obesity-Related Cardiovascular Risk Factors: A Cross-Sectional Cohort Study |
|
| Pawel Macek,Malgorzata Biskup,Malgorzata Terek-Derszniak,Michal Stachura,Halina Krol,Stanislaw Gozdz,Marek Zak | | Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy. 2020; Volume 13: 1587 | | [Pubmed] | [DOI] | | 52 |
Optimal cut-off values for anthropometric measures of obesity in screening for cardiometabolic disorders in adults |
|
| Pawel Macek,Malgorzata Biskup,Malgorzata Terek-Derszniak,Halina Krol,Jolanta Smok-Kalwat,Stanislaw Gozdz,Marek Zak | | Scientific Reports. 2020; 10(1) | | [Pubmed] | [DOI] | | 53 |
Effect of Probiotic and Synbiotic Formulations on Anthropometrics and Adiponectin in Overweight and Obese Participants: A Systematic Review and Meta-analysis of Randomized Controlled Trials |
|
| Shehua Cao,Paul M. Ryan,Ammar Salehisahlabadi,Hebatullah M. Abdulazeem,Giorgio Karam,Raminta Cerneviciute,Aleksandras Antuševas,Jamal Rahmani,Yong Zhang | | Journal of King Saud University - Science. 2020; | | [Pubmed] | [DOI] | | 54 |
Comparison of volumetric and shape changes of subcortical structures based on 3-dimensional image between obesity and normal-weighted subjects using 3.0 T MRI |
|
| A-Yoon Kim,Jae-Hyuk Shim,Hyung Jin Choi,Hyeon-Man Baek | | Journal of Clinical Neuroscience. 2020; | | [Pubmed] | [DOI] | | 55 |
Type 2 diabetes prevalence in Pakistan: what is driving this? Clues from subgroup analysis of normal weight individuals in diabetes prevalence survey of Pakistan |
|
| Azizul Hasan Aamir,Zia Ul-Haq,Sheraz Fazid,Basharat Hussain Shah,Abbas Raza,Ali Jawa,Saeed A. Mahar,Ibrar Ahmad,Faisal Masood Qureshi,Adrian H. Heald | | Cardiovascular Endocrinology & Metabolism. 2020; 9(4): 159 | | [Pubmed] | [DOI] | | 56 |
Relationship between prenatal and postnatal conditions and accelerated postnatal growth. Impact on the rigidity of the arterial wall and obesity in childhood |
|
| A. I. Mora-Urda,P. Acevedo,M. P. Montero López | | Journal of Developmental Origins of Health and Disease. 2019; 10(4): 436 | | [Pubmed] | [DOI] | | 57 |
Comparison of obesity classification methods among college students |
|
| Oliver W.A. Wilson,Zi Hua Zou,Melissa Bopp,Christopher M. Bopp | | Obesity Research & Clinical Practice. 2019; | | [Pubmed] | [DOI] | | 58 |
Prepregnancy Fat Free Mass and Associations to Glucose Metabolism Before and During Pregnancy |
|
| Eva Carolina Diaz,Elisabet Børsheim,Kartik Shankar,Mario Alberto Cleves,Aline Andres | | The Journal of Clinical Endocrinology & Metabolism. 2019; 104(5): 1394 | | [Pubmed] | [DOI] | | 59 |
Analysis of Antioxidant Consumption, Body Mass Index and the Waist-Hip Ratio in Early Postmenopause |
|
| Carlos A. Jiménez-Zamarripa,Liliana Anguiano-Robledo,Patricia Loranca-Moreno,M. Esther Ocharan-Hernández,Claudia C. Calzada-Mendoza | | Medical Sciences. 2019; 7(1): 4 | | [Pubmed] | [DOI] | | 60 |
Metabolic Syndrome—An Emerging Constellation of Risk Factors: Electrochemical Detection Strategies |
|
| Madhurantakam Sasya,K. S. Shalini Devi,Jayanth K. Babu,John Bosco Balaguru Rayappan,Uma Maheswari Krishnan | | Sensors. 2019; 20(1): 103 | | [Pubmed] | [DOI] | | 61 |
Association between helminth infections and diabetes mellitus in adults from the Lao People’s Democratic Republic: a cross-sectional study |
|
| Nan Shwe Nwe Htun,Peter Odermatt,Phimpha Paboriboune,Somphou Sayasone,Malisa Vongsakid,Vilayouth Phimolsarn-Nusith,Xuan Duong Tran,Phoum-Savath Ounnavong,Navalone Andriama-Hefasoa,Nilun-Done Senvanpan,Anousine Homsana,Baocher Lianosay,Dalouny Xayavong,Dimbitsoa Rakotomalala Robinson,Phaivanh Bounsavath,Phoy-Phaylinh Prasayasith,Seng-Davanh Syphan,Yi-Xiao Lu,Kanchana Thilakoun,Xaipa-Song Xaiyaphet,Phout-Tasin Vongngakesone,Ikenna C Eze,Medea Imboden,Banchob Sripa,Daniel Reinharz,Nicole Probst-Hensch | | Infectious Diseases of Poverty. 2018; 7(1) | | [Pubmed] | [DOI] | | 62 |
Effect of FTO
rs9930506 on obesity and interaction of the gene variants with dietary protein and vitamin E on C-reactive protein levels in multi-ethnic Malaysian adults |
|
| S. R. Mitra,P. Y. Tan,F. Amini | | Journal of Human Nutrition and Dietetics. 2018; | | [Pubmed] | [DOI] | |
|
 |
 |
|