Body Fat Percentage and Factors Associated with Cholesterol Levels among Patients at Healthy Choice Clinic in Kathmandu, Nepal: A Cross-Sectional Study

Body Fat Percentage and Factors Associated with Cholesterol Levels among Patients at Healthy Choice Clinic in Kathmandu, Nepal: A Cross-Sectional Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Body Fat Percentage and Factors Associated with Cholesterol Levels among Patients at Healthy Choice Clinic in Kathmandu, Nepal: A Cross-Sectional Study Ayush Adhikari, Rubina Karki, George Chikondi Samu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9256504/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 10 You are reading this latest preprint version Abstract Background The study of body composition and its correlation with lipid profiles are significant markers for the cardiovascular risk factors of the urban Nepali population. This study is an attempt to evaluate the percentage of body fat and determine the parameters that influence the cholesterol levels in the patients attending at Healthy Choice Clinic, Kathmandu, Nepal. Methods A quantitative cross-sectional study was conducted among 106 adult patients of Healthy Choice Clinic aged 18 years and above. Their socio-demographic profile, lifestyle and dietary practice were sourced through structured interview questionnaire. Body Mass Index (BMI), waist-to-hip ratio (WHR), and percentage of body fat were measured through Bioelectrical Impedance Analysis (BIA). For total cholesterol, triglyceride, HDL, LDL and VLDL assessment, blood sample was taken through laboratory tests. Chi-square and fisher’s exact test were used for correlation. Results The statistical prevalence showed that the overweight and obese respondent was 51.9%, and 21.7% obese. In addition, about 86.8% respondents had significant abdominal obesity with high waist to hip ratio (WHR). Of which, alcohol intake significantly correlated with high total cholesterol level among other respondents (p = 0.044). The body mass index (BMl) was significant with total cholesterol level (p = 0.022). The body mass index (BMI), WHR, and body fat percentage were all significantly correlated with high triglycerides (p = 0.019, p = 0.004, p = 0.011; respectively). Regular exercise significantly correlates with reduced very-low-density lipoprotein (VLDL). No significant association was reported between cholesterol and fat percentage and WHR and most of the dietary variables. Conclusions Although we could not find a strong correlation between body fat percentage (BFP) and cholesterol levels, BMI and central obesity indicators can provide valuable correlation with CV risk relating to dyslipidemia. Physical activity is shown to have a protective effect against dyslipidemia. These results can support the significance of a detailed clinical assessment with anthropometric measurements and lifestyle history to identify and manage patients with risk factors for CV disease. Body fat percentage Cholesterol Bioelectrical impedance analysis Lipid profile Obesity Cardiovascular risk Nepal Background The impact of body composition assessment on health prediction is crucial to health risk screening. Due to the increasing number of obesity and obesity-related diseases around the globe, the application of body composition assessment in clinical and research activities is becoming highly significant ( 1 ). Body fat percentage (BF%) means one’s health and it directly correlates to an individuals’ conditions. High BF% makes an individual susceptible to chronic health diseases such as cardiovascular diseases, diabetes mellitus, and metabolic syndrome as compared to minimum BF% ( 2 ). Obesity is an excess of adipose tissue. The mechanisms of obesity are associated with a positive energy balance ( 3 ). In 2016, there were more than 1.9 billion adults aged 18 years and older with overweight, of which more than 650 million were obese( 4 ). In Nepal NCDs are responsible for 66% of total deaths (2019) which was 60% in 2014. ( 5 ). Cardiovascular diseases in particular account for 22% of all deaths, ischemic heart disease still one of the two leading causes of death with chronic obstructive pulmonary disease. Bioelectrical Impedance Analysis (BIA) is a simple, non-invasive, fast, inexpensive, and widely used technique that estimates body composition (fat mass, fat-free mass, and body water) by measuring the impedance and reactivity of the body to a small harmless electrical current. It is clinically used to determine hydration and muscle composition, as well as cell health( 6 ). Bioelectrical impedance analysis (BIA) is a method that applies a low-level current to the study subject whose bioimpedance is to be measured. The reactance and resistance of the subject is then measured which gets the electrical conductivity of the study subject. This measure is then used to estimate body composition including the estimation of total body water, extracellular water, intracellular water, FM, fat-free mass, body fat percentage and bone mineral density ( 7 ). In addition to this, it is stated that the risk of metabolic syndrome in individuals with normal Body Mass Index (BMI), but with high body fat is four times higher than in individuals with normal BMI and low body fat( 8 ). It was recently proposed that health outcomes may be better evaluated by using a measure of body composition, rather than just BMI( 9 ). The determination of the body fatness accurately and analysis of the factors related to the body fat percentage could provide significant clinical significance to the health professionals. It could enhance the risk of determining the disease at a higher priority level especially in people with obesity and could help formulate more preventive measures to combat morbidity and mortality ( 10 ). The objective of this study was to determine the percentage of body fat and the factors associated with the cholesterol levels in clients of Healthy Choice Clinic, Kathmandu, Nepal. Methods Study design and setting The study design was a cross-sectional, conducted in Healthy Choice Clinic, located in Kathmandu metropolitan city of the country Nepal. The duration of the study was six month which includes two month of data collection. Study population and sampling The study was conducted on patients of age group of 18 years and above visiting at Healthy Choice Clinic for diet consultation. Through purposive sampling 106 respondents were recruited for the study. Sample size was calculated through the formula for infinite population n = (z²pq)/d² where z = 1.96 (confidence interval of 95%), p = 0.50 (prevalence) and d = 0.10 (margin of error). Sample size of study calculated is 96, considering 10% as non-response rate the final sample size is 106. Inclusion criteria were Inclusion criteria were adult patients (≥ 18 years) who came for diet counseling, were communicated with in the interview and agreed to provide blood sample for cholesterol analysis. Exclusion criteria were pregnant women and lactating mothers, patients unable to stand and amputated patients. Data collection Data were collected using a pre-coded semi-structured questionnaire partially adopted from the Nepal Demographic and Health Survey (NDHS) 2016. The questionnaire included sections on socio-demographic characteristics, lifestyle behaviors (exercise habits, smoking, alcohol consumption), and dietary patterns. A semi structured questionnaire was used which was pre-coded and adapted from the course of Nepal Demographic and Health Survey (NDHS) 2016. The questionnaire consists of socio-demographic characteristics, lifestyle pattern including physical activity, smoking, alcohol drinking etc. and eating habit. Anthropometric data like height (made using stadiometer),weight, BMI, body fat percentage, waist to hip ratio was obtained by Bioelectrical Impedance Analysis (BIA) using New Angie GS6.7 (2019, manufactured in China). BIA machine produces electrical current of low-level and sends it through body to obtain electrical impedance from which body fat percentage and other characteristics can be calculated. Due to more water content, lean tissues have higher conductivity compared to fat tissues and these parameters can be calculated from the machine to obtain total body water, fat free body mass and fat mass. Blood samples were drawn for Lipid profile, which includes Total cholesterol, Triglycerides, High density lipoprotein (HDL), Low density lipoprotein (LDL), and very low density lipoprotein (VLDL) was performed. Reference ranges are as follows: Total cholesterol (< 200 mg/dL); Triglycerides ( 35 mg/dL); LDL (< 100 mg/dL); and VLDL (< 30 mg/dL). Statistical analysis The data was entered in the excel sheet and analyzed through SSPS statistical software. Descriptive statistics was done to present the data regarding socio-demographic and other characteristics. Chi-square test and Fisher’s exact tests were applied to analyze the relationships and p < 0.05 was considered significant. Ethical considerations Permission was obtained from The CAFODAT college Research Committee for carrying out the study. Verbal and written approval was obtained from the clinic’s administration for conducting this study. Informed consent was taken from the study participants after explaining the aim and significance about the study. The confidentiality of the participants was maintained and secured. Results Socio-demographic characteristics The study consisted of 106 subjects. The age of the subjects ranged from 18 year to 60 years with a mean age of 29.91 years (SD = 8.40). The maximum age group surveyed was 18–29 (56.6%) followed by 30–39 years (20.8%), 40–49 years (9.4%) and 50–60 years (3.8%). The subjects comprised of 52.8% females and 47.2% males. The caste/ethnic composition of the subjects was Brahmin/Chhetri (50%), Janajati (36.8%), Madheshi (6.6%) and Others (4.7%). By educational status, 98.1% had superior secondary level education. In case of employment, majority of the participants were working in the private sector (62.3%), government (17.9%), unemployed (14.2%), and housewives (5.7%) respectively. Majority of the participants (n = 249, 83%) were from a nuclear family (Table 1 ). Table 1 Socio-demographic characteristics of the respondents (n = 106) Characteristics Frequency Percent (%) Age (years) 18–29 60 56.6 30–39 22 20.8 40–49 10 9.4 50–60 4 3.8 Sex Male 50 47.2 Female 56 52.8 Ethnicity Brahmin/Chhetri 53 50.0 Janajati 39 36.8 Madheshi 7 6.6 Others 5 4.7 Education Higher secondary 104 98.1 Occupation Private job 66 62.3 Government job 19 17.9 Unemployed 15 14.2 Homemaker 6 5.7 Family type Nuclear 88 83.0 Joint 18 17.0 Lifestyle characteristics In terms of physical activity, the participants were 79.2% non-exercisers and 20.8% exercisers. With regards to smoking status, the subjects were 84% non-smokers and 16% smokers. According to alcohol drinking status, the participants were 64.2% non-drinkers and 35.8% alcohol-drinkers. Most of the participants were non-vegetarians (88.7%), while the vegetarians constituted minority (11.3%) (Table 2 ). Table 2 Lifestyle characteristics of the respondents (n = 106) Characteristics Frequency Percent (%) Exercise habits Yes 22 20.8 No 84 79.2 Smoking habit Yes 17 16.0 No 89 84.0 Alcohol consumption Yes 38 35.8 No 68 64.2 Dietary habit Non-vegetarian 94 88.7 Vegetarian 12 11.3 Anthropometric measurements In terms of BMI, 51.9% of participants were overweight, followed by 21.7% with Obese I classification. Only 16% had normal BMI, while 6.6%, 2.8%, and 0.9% of respondents were underweight, Obese II, and Obese III respectively. Most of the respondents had high waist-hip ratio (WHR) at 86.8%, which corresponds with increased risk of ailments associated with central obesity. Only 13.2% had normal WHR (Table 3 ). Table 3 Anthropometric measurements of the respondents (n = 106) Measurement Category Frequency Percent (%) BMI Underweight 7 6.6 Normal 17 16.0 Overweight 55 51.9 Obese I 23 21.7 Obese II 3 2.8 Obese III 1 0.9 WHR Normal 14 13.2 High 92 86.8 Body fat percentage Non-obese 45 42.5 Obese 61 57.5 Associations with total cholesterol Among total of 106 respondents, 103 (97.2) had normal cholesterol level and 3 (2.8%) had high cholesterol. There is a significant association between alcohol intake and their total cholesterol level (p-value = 0.044). Among 38 alcohol drinkers, 3 (7.9%) had high cholesterol and none of the 68 respondents are alcohol non-drinkers. There is a significant relation between BMI and Total Cholesterol (p = 0.022). All the underweight, normal weight and overweight individuals had normal Total Cholesterol. However, the elevated level of Cholesterol was found in 4.3% of Obese I, 14.3% of Obese II and 33.3% of Obese III, with significant increasing trend (Table 4 ). No significant relationship was found between WHR and total cholesterol (p = 1.000) and percentage body fat and total cholesterol (p = 0.260). None of the dietary variables had significant associations with total cholesterol levels. Table 4 Association of anthropometric measurements with total cholesterol (n = 106) Variable Normal n (%) High n (%) χ² p-value BMI 15.809 0.022* Underweight 7 (100) 0 (0) Normal 17 (100) 0 (0) Overweight 55 (100) 0 (0) Obese I 22 (95.7) 1 (4.3) Obese II 6 (85.7) 1 (14.3) Obese III 2 (66.7) 1 (33.3) WHR 0.470 1.000* Normal 14 (100) 0 (0) High 89 (96.7) 3 (3.3) Body fat percentage 2.278 0.260* Non-obese 45 (100) 0 (0) Obese 58 (95.1) 3 (4.9) *Fisher's Exact Test Associations with triglycerides Family type had a significant association with triglycerides levels and the p-value is 0.010. The joint family members demonstrated greater percentages of high triglycerides (p = 0.010). However, significantly higher proportion of participants from joint families had high triglyceride level (77.8%) as compared to participants with nuclear families (44.3%). There is a significant association between anthropometric measures and triglycerides. Triglycerides had a significant association with BMI (p = 0.019); a high BMI correlated to high triglycerides. Triglycerides are significantly associated with WHR (p = 0.004) as 55.4% of participants with elevated WHR have elevated triglyceride level in comparison to 14.3% with normal WHR. Body fat percentage also shows significant association (p = 0.011), as 60.7% of obese with elevated triglycerides compared to 35.6% of non-obese (Table 5 ). Table 5 Association of anthropometric measurements with triglyceride levels (n = 106) Variable Normal n (%) High n (%) χ² p-value BMI 11.767 0.019* Underweight 1 (100) 0 (0) Normal 11 (64.7) 6 (35.3) Overweight 32 (58.2) 23 (41.8) Obese I 5 (21.7) 18 (78.3) Obese II 3 (42.9) 4 (57.1) Obese III 1 (33.3) 2 (66.7) WHR 8.230 0.004 Normal 12 (85.7) 2 (14.3) High 41 (44.6) 51 (55.4) Body fat percentage 6.526 0.011 Non-obese 29 (64.4) 16 (35.6) Obese 24 (39.3) 37 (60.7) *Fisher's Exact Test Associations with HDL, LDL, and VLDL Age (p = 0.033) was significantly associated with HDL level; younger individuals were more likely to have a higher HDL. Consumption of white potatoes (p = 0.020) and sugary drinks (p = 0.009) were significantly associated with HDL level. Statistically significant relation of age was found with LDL (p = 0.026). Individuals with age of 30–49 years had the highest LDL (Table 6 ). For VLDL, the gender (p = 0.046), occupation (p = 0.034) and exercise pattern of the subjects (p = 0.001) are significantly correlated. Exercising was found to be protective as 18.2% of the subjects exercising had high VLDL levels in comparison to 63.1% of the non-exercisers. Also, amongst the three measurements, VLDL was significantly related to BMI (p = 0.024), WHR (p = 0.002) and body fat (p = 0.006) (Table 7 ). Table 6 Association of lifestyle factors with VLDL levels (n = 106) Variable Normal n (%) High n (%) χ² p-value Exercise habit 14.148 0.001* Yes 18 (81.8) 4 (18.2) No 31 (36.9) 53 (63.1) Smoking habit 2.303 0.185* Yes 5 (29.4) 12 (70.6) No 44 (49.4) 45 (50.6) Alcohol consumption 0.405 0.549* Yes 16 (42.1) 22 (57.9) No 33 (48.5) 35 (51.5) *Fisher's Exact Test Table 7 Association of anthropometric measurements with VLDL levels (n = 106) Variable Normal n (%) High n (%) χ² p-value BMI 11.418 0.024* Underweight 1 (100) 0 (0) Normal 13 (76.5) 4 (23.5) Overweight 25 (45.5) 30 (54.5) Obese I 6 (26.1) 17 (73.9) Obese II 3 (42.9) 4 (57.1) Obese III 1 (33.3) 2 (66.7) WHR 10.118 0.002* Normal 12 (85.7) 2 (14.3) High 37 (40.2) 55 (59.8) Body fat percentage 8.049 0.006* Non-obese 28 (62.2) 17 (37.8) Obese 21 (34.4) 40 (65.6) *Fisher's Exact Test Discussion The relationship of body fat percentage and cholesterol among patients of Healthy Choice Clinic, Kathmandu was assessed through this cross-sectional study with 106 adult patients. This study provided useful correlations of anthropometric, lifestyle and lipid profile characteristics of patients in current urban Nepali setting. Overweight (51.9%) and obesity (21.7%) were prevalent among the study participants, with 86.8% having WHR above normal. The observation is consistent with existing literature corroborating urbanization, sedentary lifestyle, and diet transition as a significant marker for the alarming obesity status in the South Asian population( 11 ). Also, due to its relationship with cardiovascular risk, central obesity observed as WHR above normal, is alarming( 12 ). The direct association of increased total cholesterol with alcohol intake (p = 0.044) has been established in previous studies. As per Khanal et al. population of Nepal showed high frequency of alcohol use was directly linked with dyslipidemia due to the effect on lipid metabolism in liver ( 13 ). This establishes that importance of alcohol use should be targeted in order to control cholesterol levels. The positive significant correlation of BMI with total cholesterol (p = 0.022) which is significantly higher in higher categories of obesity, also supports known association of hyperlipidemia with increasing adiposity. Similar relationship has been reported by Aryal et al., where the authors found strong association between obesity and dyslipidemia in Nepalese population( 14 ). It is interesting that WHR and body fat percentage did not show any significant association with total cholesterol. This implies that even though, central obesity and overall body fat percentage might affect the risk of getting cardiovascular diseases, they might not be indicative of the total cholesterol levels especially in populations with younger mean age such as the cohort studied here. This is due to the complexity of lipid metabolism that requires different indicators and assessment measures. According to a study conducted in 2018 by Khanal et al., it was found that overweight and obese populations in rural Nepal had higher prevalence of hypertriglyceridemia (27.2%) and that there was a positive association of waist circumference and BMI with triglycerides( 15 ). Regular physical activity is well investigated factor with protective effect against high levels of VLDL (p = 0.011). The clinical and cardiovascular benefits of habitual exercise in reduction of LDL and stimulating HDL is majorly discussed in study done by Thompson et al( 16 ). Supportive to this finding, vigorous physical exercise is encouraged as major intervention in affairs of reducing cardiovascular risk. Most of the dietary components were not significantly correlated to lipid profile. This could have been due to small sample size or grouping may did not fulfill sufficient statistical power. The sample utilized in this analysis may also not have representative effect to the general population in Nepal because of young adults and well educated college students. Conclusions This study thus reveals important knowledge about links of body compositions and lifestyle factors with lipid profile in urban Nepali adults. Total cholesterol may not be estimated individually by body fat percentage, however, estimation from BMI and central obesity markers together have a better predictive role in lipid-associated cardiovascular risks. Given the significant associations of total cholesterol with alcohol and BMI and triglycerides and VLDL with all circumferential measures, it signifies the effectiveness of controlling weight and lifestyle changes in preventing cardiovascular diseases. In conclusion, evidence showed that daily exercise was solidly protective against high VLDL which highlighted exercise was an effective therapeutic approach. With above evidence, it would be useful if clinician could combine detailed clinical assessments including BMI, waist-hip ratio, and clinical evaluation on nutritional habit and physical activity to identify patients with risk of dyslipidemia. Abbreviations BIA Bioelectrical Impedance Analysis BMI Body Mass Index CVD Cardiovascular Disease HDL High-Density Lipoprotein LDL Low-Density Lipoprotein NCDs Non-Communicable Diseases VLDL Very Low-Density Lipoprotein WHR Waist-to-Hip Ratio Declarations Ethics approval and consent to participate The College of applied food and dairy technology ( CAFODAT) Research Committee approved the study protocol. A written informed consent form was provided to each participant. The study protocol was implemented in accordance with the relevant guidelines and regulations. The person from the ethics committee responsible for the approval of this research is Mr. Parbat Thapa Magar. Publication declaration The authors declare that this research has not been published in any other journal, therefore it is declared to comply with BMC publications policy. There are no images used, or other materials which require anonymity, the questionnaire followed informed consent and anonymity. Consent for publication Not applicable Availability of data and materials The datasets that were used and/or analyzed during the current study are available from the corresponding author on reasonable request. Competing interests The author has no conflicts of interest to disclose. Funding The author declares no funding received for this study. Authors' contributions Mr. Ayush Adhikari conceived the study, acquired, analyzed, and interpreted data. Ms. Rubina drafted the manuscript and Mr. George contributed in data tabulation, presentation and final editing. Author has read and approved the final manuscript. Acknowledgements The author would like to express gratitude to Mr. Bhupal Baniya for supervision and guidance, Prof. Kalpana Tiwari for valuable input, and the management team of Healthy Choice Clinic for providing consent and opportunity to conduct this research. References Obesity. and overweight [Internet]. [cited 2026 Mar 8]. Available from: https://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight Després JP, Lemieux I. Abdominal obesity and metabolic syndrome. Nature. 2006;444(7121):881–7. 10.1038/nature05488 . PubMed PMID: 17167477. Kim HY, Kim JK, Shin GG, Han JA, Kim JW. Association between Abdominal Obesity and Cardiovascular Risk Factors in Adults with Normal Body Mass Index: Based on the Sixth Korea National Health and Nutrition Examination Survey. J Obes Metabolic Syndrome. 2019;28(4):262. 10.7570/jomes.2019.28 . .4.262 PubMed PMID: 31909369. Obesity [Internet]. [cited 2026 Mar 8]. 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Body Mass Index vs Body Fat Percentage as a Predictor of Mortality in Adults Aged 20–49 Years. Annals Family Med. 2025;23(4):337–43. 10.1370/afm.240330 . PubMed PMID: 40555527. Holmes CJ, Racette SB. The Utility of Body Composition Assessment in Nutrition and Clinical Practice: An Overview of Current Methodology. Nutrients. 2021;13(8). 10.3390/nu13082493 . Misra A, Khurana L. Obesity and the metabolic syndrome in developing countries. J Clin Endocrinol Metab. 2008;93(11 Suppl 1):S9–30. 10.1210/jc.2008-1595 . PubMed PMID: 18987276. Cj D, Mr J, Dr M. A comparative evaluation of waist circumference, waist-to-hip ratio and body mass index as indicators of cardiovascular risk factors. Can Heart Health Surv Int J Obes Relat metabolic disorders: J Int Association Study Obes. 2001;25(5). 10.1038/sj.ijo.0801582 . PubMed PMID: 11360147. Khanal MK, Mansur Ahmed MSA, Moniruzzaman M, Banik PC, Dhungana RR, Bhandari P, et al. Prevalence and clustering of cardiovascular disease risk factors in rural Nepalese population aged 40–80 years. BMC Public Health. 2018;18(1):677. 10.1186/s12889-018-5600-9 . PubMed PMID: 29855293; PubMed Central PMCID: PMC5984400. Aryal KK, Neupane S, Mehata S, Vaidya A, Sinha DN. Non Communicable Diseases Risk Factors: STEPS Survey Nepal. 2014. Khanal MK, Ahmed MSAM, Moniruzzaman M, Banik PC, Dhungana RR, Bhandari P, et al. Prevalence and clustering of cardiovascular disease risk factors in rural Nepalese population aged 40–80 years. BMC Public Health. 2018;18:677. 10.1186/s12889-018-5600-9 . PubMed PMID: 29855293. Thompson PD, Crouse SF, Goodpaster B, Kelley D, Moyna N, Pescatello L. The acute versus the chronic response to exercise. Med Sci Sports Exerc. 2001;33(6 Suppl):S438-445; discussion S452-453. 10.1097/00005768-200106001-00012 PubMed PMID: 11427768. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 06 May, 2026 Reviews received at journal 04 May, 2026 Reviews received at journal 22 Apr, 2026 Reviewers agreed at journal 19 Apr, 2026 Reviewers agreed at journal 18 Apr, 2026 Reviewers invited by journal 15 Apr, 2026 Editor assigned by journal 15 Apr, 2026 Editor invited by journal 10 Apr, 2026 Submission checks completed at journal 09 Apr, 2026 First submitted to journal 09 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9256504","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":627951630,"identity":"b9ab93e1-8764-4711-b1a7-addc37ceb653","order_by":0,"name":"Ayush Adhikari","email":"","orcid":"","institution":"CAFODAT College, Purbanchal University","correspondingAuthor":false,"prefix":"","firstName":"Ayush","middleName":"","lastName":"Adhikari","suffix":""},{"id":627951631,"identity":"ba6d736d-af17-4063-bc71-65232be8424f","order_by":1,"name":"Rubina Karki","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2klEQVRIiWNgGAWjYFACHgbGBgYGAzaGwweAPAkZErQwHksAaeEhXgsD8xkDCJcQMJ+Re0xyRs09Yz62M59f3aix4GFgP3x0Az4tMjfy0iQ3HCs2Y+M5u8065xjQYTxpaTfwaZGQyDGTfMCWYMMmcXabcQ4bUIsEjxkRWv4Btci/eWac849YLRvbEszYGM4wP85tI0YLz7tky5l9CcZsDMfMmHP7JHjYCPqFPffgzZ5vCYbzGw4//pzzrU6On/3wMbxakAGbBJgkVjkIMH8gRfUoGAWjYBSMHAAARxFDy7M5ZOQAAAAASUVORK5CYII=","orcid":"","institution":"CAFODAT College, Purbanchal University","correspondingAuthor":true,"prefix":"","firstName":"Rubina","middleName":"","lastName":"Karki","suffix":""},{"id":627951632,"identity":"77b44f86-1f44-4012-a43c-4d33dfb7bff1","order_by":2,"name":"George Chikondi Samu","email":"","orcid":"","institution":"Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"George","middleName":"Chikondi","lastName":"Samu","suffix":""}],"badges":[],"createdAt":"2026-03-29 05:08:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9256504/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9256504/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107707538,"identity":"73214872-49dc-4c17-876a-2333197814a7","added_by":"auto","created_at":"2026-04-24 09:20:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":406512,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9256504/v1/c275d592-4900-41fd-bf33-f0cc85ed4dab.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Body Fat Percentage and Factors Associated with Cholesterol Levels among Patients at Healthy Choice Clinic in Kathmandu, Nepal: A Cross-Sectional Study","fulltext":[{"header":"Background","content":"\u003cp\u003eThe impact of body composition assessment on health prediction is crucial to health risk screening. Due to the increasing number of obesity and obesity-related diseases around the globe, the application of body composition assessment in clinical and research activities is becoming highly significant (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Body fat percentage (BF%) means one\u0026rsquo;s health and it directly correlates to an individuals\u0026rsquo; conditions. High BF% makes an individual susceptible to chronic health diseases such as cardiovascular diseases, diabetes mellitus, and metabolic syndrome as compared to minimum BF% (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eObesity is an excess of adipose tissue. The mechanisms of obesity are associated with a positive energy balance (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). In 2016, there were more than 1.9\u0026nbsp;billion adults aged 18 years and older with overweight, of which more than 650\u0026nbsp;million were obese(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). In Nepal NCDs are responsible for 66% of total deaths (2019) which was 60% in 2014. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Cardiovascular diseases in particular account for 22% of all deaths, ischemic heart disease still one of the two leading causes of death with chronic obstructive pulmonary disease.\u003c/p\u003e \u003cp\u003eBioelectrical Impedance Analysis (BIA) is a simple, non-invasive, fast, inexpensive, and widely used technique that estimates body composition (fat mass, fat-free mass, and body water) by measuring the impedance and reactivity of the body to a small harmless electrical current. It is clinically used to determine hydration and muscle composition, as well as cell health(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Bioelectrical impedance analysis (BIA) is a method that applies a low-level current to the study subject whose bioimpedance is to be measured. The reactance and resistance of the subject is then measured which gets the electrical conductivity of the study subject. This measure is then used to estimate body composition including the estimation of total body water, extracellular water, intracellular water, FM, fat-free mass, body fat percentage and bone mineral density (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn addition to this, it is stated that the risk of metabolic syndrome in individuals with normal Body Mass Index (BMI), but with high body fat is four times higher than in individuals with normal BMI and low body fat(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). It was recently proposed that health outcomes may be better evaluated by using a measure of body composition, rather than just BMI(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe determination of the body fatness accurately and analysis of the factors related to the body fat percentage could provide significant clinical significance to the health professionals. It could enhance the risk of determining the disease at a higher priority level especially in people with obesity and could help formulate more preventive measures to combat morbidity and mortality (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). The objective of this study was to determine the percentage of body fat and the factors associated with the cholesterol levels in clients of Healthy Choice Clinic, Kathmandu, Nepal.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and setting\u003c/h2\u003e \u003cp\u003eThe study design was a cross-sectional, conducted in Healthy Choice Clinic, located in Kathmandu metropolitan city of the country Nepal. The duration of the study was six month which includes two month of data collection.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy population and sampling\u003c/h3\u003e\n\u003cp\u003eThe study was conducted on patients of age group of 18 years and above visiting at Healthy Choice Clinic for diet consultation. Through purposive sampling 106 respondents were recruited for the study. Sample size was calculated through the formula for infinite population n = (z\u0026sup2;pq)/d\u0026sup2; where z\u0026thinsp;=\u0026thinsp;1.96 (confidence interval of 95%), p\u0026thinsp;=\u0026thinsp;0.50 (prevalence) and d\u0026thinsp;=\u0026thinsp;0.10 (margin of error). Sample size of study calculated is 96, considering 10% as non-response rate the final sample size is 106.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eInclusion criteria were\u003c/strong\u003e \u003cp\u003eInclusion criteria were adult patients (\u0026ge;\u0026thinsp;18 years) who came for diet counseling, were communicated with in the interview and agreed to provide blood sample for cholesterol analysis. Exclusion criteria were pregnant women and lactating mothers, patients unable to stand and amputated patients.\u003c/p\u003e \u003c/p\u003e\n\u003ch3\u003eData collection\u003c/h3\u003e\n\u003cp\u003eData were collected using a pre-coded semi-structured questionnaire partially adopted from the Nepal Demographic and Health Survey (NDHS) 2016. The questionnaire included sections on socio-demographic characteristics, lifestyle behaviors (exercise habits, smoking, alcohol consumption), and dietary patterns.\u003c/p\u003e \u003cp\u003eA semi structured questionnaire was used which was pre-coded and adapted from the course of Nepal Demographic and Health Survey (NDHS) 2016. The questionnaire consists of socio-demographic characteristics, lifestyle pattern including physical activity, smoking, alcohol drinking etc. and eating habit. Anthropometric data like height (made using stadiometer),weight, BMI, body fat percentage, waist to hip ratio was obtained by Bioelectrical Impedance Analysis (BIA) using New Angie GS6.7 (2019, manufactured in China). BIA machine produces electrical current of low-level and sends it through body to obtain electrical impedance from which body fat percentage and other characteristics can be calculated. Due to more water content, lean tissues have higher conductivity compared to fat tissues and these parameters can be calculated from the machine to obtain total body water, fat free body mass and fat mass.\u003c/p\u003e \u003cp\u003eBlood samples were drawn for Lipid profile, which includes Total cholesterol, Triglycerides, High density lipoprotein (HDL), Low density lipoprotein (LDL), and very low density lipoprotein (VLDL) was performed. Reference ranges are as follows: Total cholesterol (\u0026lt;\u0026thinsp;200 mg/dL); Triglycerides (\u0026lt;\u0026thinsp;150 mg/dL); HDL (\u0026gt;\u0026thinsp;35 mg/dL); LDL (\u0026lt;\u0026thinsp;100 mg/dL); and VLDL (\u0026lt;\u0026thinsp;30 mg/dL).\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe data was entered in the excel sheet and analyzed through SSPS statistical software. Descriptive statistics was done to present the data regarding socio-demographic and other characteristics. Chi-square test and Fisher\u0026rsquo;s exact tests were applied to analyze the relationships and p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered significant.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEthical considerations\u003c/h3\u003e\n\u003cp\u003ePermission was obtained from The CAFODAT college Research Committee for carrying out the study. Verbal and written approval was obtained from the clinic\u0026rsquo;s administration for conducting this study. Informed consent was taken from the study participants after explaining the aim and significance about the study. The confidentiality of the participants was maintained and secured.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eSocio-demographic characteristics\u003c/h2\u003e \u003cp\u003eThe study consisted of 106 subjects. The age of the subjects ranged from 18 year to 60 years with a mean age of 29.91 years (SD\u0026thinsp;=\u0026thinsp;8.40). The maximum age group surveyed was 18\u0026ndash;29 (56.6%) followed by 30\u0026ndash;39 years (20.8%), 40\u0026ndash;49 years (9.4%) and 50\u0026ndash;60 years (3.8%). The subjects comprised of 52.8% females and 47.2% males. The caste/ethnic composition of the subjects was Brahmin/Chhetri (50%), Janajati (36.8%), Madheshi (6.6%) and Others (4.7%).\u003c/p\u003e \u003cp\u003eBy educational status, 98.1% had superior secondary level education. In case of employment, majority of the participants were working in the private sector (62.3%), government (17.9%), unemployed (14.2%), and housewives (5.7%) respectively. Majority of the participants (n\u0026thinsp;=\u0026thinsp;249, 83%) were from a nuclear family (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSocio-demographic characteristics of the respondents (n\u0026thinsp;=\u0026thinsp;106)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercent (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u0026ndash;29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e56.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u0026ndash;39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40\u0026ndash;49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50\u0026ndash;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e52.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEthnicity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBrahmin/Chhetri\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJanajati\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMadheshi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigher secondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e98.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOccupation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrivate job\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e62.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGovernment job\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnemployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHomemaker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNuclear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e83.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJoint\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eLifestyle characteristics\u003c/h3\u003e\n\u003cp\u003eIn terms of physical activity, the participants were 79.2% non-exercisers and 20.8% exercisers. With regards to smoking status, the subjects were 84% non-smokers and 16% smokers. According to alcohol drinking status, the participants were 64.2% non-drinkers and 35.8% alcohol-drinkers. Most of the participants were non-vegetarians (88.7%), while the vegetarians constituted minority (11.3%) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLifestyle characteristics of the respondents (n\u0026thinsp;=\u0026thinsp;106)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercent (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExercise habits\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e79.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking habit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e84.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol consumption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e64.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDietary habit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-vegetarian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e88.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVegetarian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eAnthropometric measurements\u003c/h2\u003e \u003cp\u003eIn terms of BMI, 51.9% of participants were overweight, followed by 21.7% with Obese I classification. Only 16% had normal BMI, while 6.6%, 2.8%, and 0.9% of respondents were underweight, Obese II, and Obese III respectively. Most of the respondents had high waist-hip ratio (WHR) at 86.8%, which corresponds with increased risk of ailments associated with central obesity. Only 13.2% had normal WHR (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAnthropometric measurements of the respondents (n\u0026thinsp;=\u0026thinsp;106)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeasurement\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePercent (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnderweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e51.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObese I\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObese II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObese III\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e86.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody fat percentage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-obese\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e42.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObese\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e57.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eAssociations with total cholesterol\u003c/h2\u003e \u003cp\u003eAmong total of 106 respondents, 103 (97.2) had normal cholesterol level and 3 (2.8%) had high cholesterol. There is a significant association between alcohol intake and their total cholesterol level (p-value\u0026thinsp;=\u0026thinsp;0.044). Among 38 alcohol drinkers, 3 (7.9%) had high cholesterol and none of the 68 respondents are alcohol non-drinkers.\u003c/p\u003e \u003cp\u003eThere is a significant relation between BMI and Total Cholesterol (p\u0026thinsp;=\u0026thinsp;0.022). All the underweight, normal weight and overweight individuals had normal Total Cholesterol. However, the elevated level of Cholesterol was found in 4.3% of Obese I, 14.3% of Obese II and 33.3% of Obese III, with significant increasing trend (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNo significant relationship was found between WHR and total cholesterol (p\u0026thinsp;=\u0026thinsp;1.000) and percentage body fat and total cholesterol (p\u0026thinsp;=\u0026thinsp;0.260). None of the dietary variables had significant associations with total cholesterol levels.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation of anthropometric measurements with total cholesterol (n\u0026thinsp;=\u0026thinsp;106)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal n (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh n (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eχ\u0026sup2;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.809\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.022*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnderweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObese I\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22 (95.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (4.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObese II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (85.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (14.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObese III\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (66.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (33.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.470\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.000*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e89 (96.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (3.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody fat percentage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.260*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-obese\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObese\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58 (95.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (4.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e*Fisher's Exact Test\u003c/h2\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003eAssociations with triglycerides\u003c/h2\u003e \u003cp\u003eFamily type had a significant association with triglycerides levels and the p-value is 0.010. The joint family members demonstrated greater percentages of high triglycerides (p\u0026thinsp;=\u0026thinsp;0.010). However, significantly higher proportion of participants from joint families had high triglyceride level (77.8%) as compared to participants with nuclear families (44.3%).\u003c/p\u003e \u003cp\u003eThere is a significant association between anthropometric measures and triglycerides. Triglycerides had a significant association with BMI (p\u0026thinsp;=\u0026thinsp;0.019); a high BMI correlated to high triglycerides. Triglycerides are significantly associated with WHR (p\u0026thinsp;=\u0026thinsp;0.004) as 55.4% of participants with elevated WHR have elevated triglyceride level in comparison to 14.3% with normal WHR. Body fat percentage also shows significant association (p\u0026thinsp;=\u0026thinsp;0.011), as 60.7% of obese with elevated triglycerides compared to 35.6% of non-obese (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation of anthropometric measurements with triglyceride levels (n\u0026thinsp;=\u0026thinsp;106)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal n (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh n (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eχ\u0026sup2;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.767\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.019*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnderweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (64.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (35.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32 (58.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23 (41.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObese I\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (21.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (78.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObese II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (42.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (57.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObese III\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (33.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (66.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (85.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (14.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41 (44.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51 (55.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody fat percentage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.526\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-obese\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29 (64.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (35.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObese\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24 (39.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37 (60.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e*Fisher's Exact Test\u003c/h2\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003eAssociations with HDL, LDL, and VLDL\u003c/h2\u003e \u003cp\u003eAge (p\u0026thinsp;=\u0026thinsp;0.033) was significantly associated with HDL level; younger individuals were more likely to have a higher HDL. Consumption of white potatoes (p\u0026thinsp;=\u0026thinsp;0.020) and sugary drinks (p\u0026thinsp;=\u0026thinsp;0.009) were significantly associated with HDL level.\u003c/p\u003e \u003cp\u003eStatistically significant relation of age was found with LDL (p\u0026thinsp;=\u0026thinsp;0.026). Individuals with age of 30\u0026ndash;49 years had the highest LDL (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFor VLDL, the gender (p\u0026thinsp;=\u0026thinsp;0.046), occupation (p\u0026thinsp;=\u0026thinsp;0.034) and exercise pattern of the subjects (p\u0026thinsp;=\u0026thinsp;0.001) are significantly correlated. Exercising was found to be protective as 18.2% of the subjects exercising had high VLDL levels in comparison to 63.1% of the non-exercisers. Also, amongst the three measurements, VLDL was significantly related to BMI (p\u0026thinsp;=\u0026thinsp;0.024), WHR (p\u0026thinsp;=\u0026thinsp;0.002) and body fat (p\u0026thinsp;=\u0026thinsp;0.006) (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation of lifestyle factors with VLDL levels (n\u0026thinsp;=\u0026thinsp;106)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal n (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh n (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eχ\u0026sup2;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExercise habit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18 (81.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4 (18.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31 (36.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e53 (63.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking habit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.303\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.185*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5 (29.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12 (70.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e44 (49.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e45 (50.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol consumption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.405\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.549*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16 (42.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22 (57.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e33 (48.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35 (51.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e*Fisher's Exact Test\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation of anthropometric measurements with VLDL levels (n\u0026thinsp;=\u0026thinsp;106)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal n (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh n (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eχ\u0026sup2;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.418\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.024*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnderweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (76.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (23.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25 (45.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30 (54.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObese I\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (26.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (73.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObese II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (42.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (57.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObese III\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (33.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (66.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (85.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (14.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37 (40.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55 (59.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody fat percentage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.006*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-obese\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28 (62.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (37.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObese\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21 (34.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40 (65.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e*Fisher's Exact Test\u003c/h2\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe relationship of body fat percentage and cholesterol among patients of Healthy Choice Clinic, Kathmandu was assessed through this cross-sectional study with 106 adult patients. This study provided useful correlations of anthropometric, lifestyle and lipid profile characteristics of patients in current urban Nepali setting.\u003c/p\u003e \u003cp\u003eOverweight (51.9%) and obesity (21.7%) were prevalent among the study participants, with 86.8% having WHR above normal. The observation is consistent with existing literature corroborating urbanization, sedentary lifestyle, and diet transition as a significant marker for the alarming obesity status in the South Asian population(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Also, due to its relationship with cardiovascular risk, central obesity observed as WHR above normal, is alarming(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe direct association of increased total cholesterol with alcohol intake (p\u0026thinsp;=\u0026thinsp;0.044) has been established in previous studies. As per Khanal et al. population of Nepal showed high frequency of alcohol use was directly linked with dyslipidemia due to the effect on lipid metabolism in liver (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). This establishes that importance of alcohol use should be targeted in order to control cholesterol levels.\u003c/p\u003e \u003cp\u003eThe positive significant correlation of BMI with total cholesterol (p\u0026thinsp;=\u0026thinsp;0.022) which is significantly higher in higher categories of obesity, also supports known association of hyperlipidemia with increasing adiposity. Similar relationship has been reported by Aryal et al., where the authors found strong association between obesity and dyslipidemia in Nepalese population(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIt is interesting that WHR and body fat percentage did not show any significant association with total cholesterol. This implies that even though, central obesity and overall body fat percentage might affect the risk of getting cardiovascular diseases, they might not be indicative of the total cholesterol levels especially in populations with younger mean age such as the cohort studied here. This is due to the complexity of lipid metabolism that requires different indicators and assessment measures.\u003c/p\u003e \u003cp\u003eAccording to a study conducted in 2018 by Khanal et al., it was found that overweight and obese populations in rural Nepal had higher prevalence of hypertriglyceridemia (27.2%) and that there was a positive association of waist circumference and BMI with triglycerides(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRegular physical activity is well investigated factor with protective effect against high levels of VLDL (p\u0026thinsp;=\u0026thinsp;0.011). The clinical and cardiovascular benefits of habitual exercise in reduction of LDL and stimulating HDL is majorly discussed in study done by Thompson et al(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Supportive to this finding, vigorous physical exercise is encouraged as major intervention in affairs of reducing cardiovascular risk.\u003c/p\u003e \u003cp\u003eMost of the dietary components were not significantly correlated to lipid profile. This could have been due to small sample size or grouping may did not fulfill sufficient statistical power. The sample utilized in this analysis may also not have representative effect to the general population in Nepal because of young adults and well educated college students.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study thus reveals important knowledge about links of body compositions and lifestyle factors with lipid profile in urban Nepali adults. Total cholesterol may not be estimated individually by body fat percentage, however, estimation from BMI and central obesity markers together have a better predictive role in lipid-associated cardiovascular risks. Given the significant associations of total cholesterol with alcohol and BMI and triglycerides and VLDL with all circumferential measures, it signifies the effectiveness of controlling weight and lifestyle changes in preventing cardiovascular diseases.\u003c/p\u003e \u003cp\u003eIn conclusion, evidence showed that daily exercise was solidly protective against high VLDL which highlighted exercise was an effective therapeutic approach. With above evidence, it would be useful if clinician could combine detailed clinical assessments including BMI, waist-hip ratio, and clinical evaluation on nutritional habit and physical activity to identify patients with risk of dyslipidemia.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eBIA\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBioelectrical Impedance Analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eBMI\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBody Mass Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eCVD\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCardiovascular Disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eHDL\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHigh-Density Lipoprotein\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eLDL\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLow-Density Lipoprotein\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eNCDs\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNon-Communicable Diseases\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eVLDL\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eVery Low-Density Lipoprotein\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eWHR\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWaist-to-Hip Ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eThe College of applied food and dairy technology ( CAFODAT) Research Committee approved the study protocol. A written informed consent form was provided to each participant. The study protocol was implemented in accordance with the relevant guidelines and regulations. The person from the ethics committee responsible for the approval of this research is Mr. Parbat Thapa Magar.\u003c/p\u003e\n\u003cp\u003ePublication declaration\u003c/p\u003e\n\u003cp\u003eThe authors declare that this research has not been published in any other journal, therefore it is declared to comply with BMC publications policy. There are no images used, or other materials which require anonymity, the questionnaire followed informed consent and anonymity.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials\u003c/p\u003e\n\u003cp\u003eThe datasets that were used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe author has no conflicts of interest to disclose.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThe author declares no funding received for this study.\u003c/p\u003e\n\u003cp\u003eAuthors\u0026apos; contributions\u003c/p\u003e\n\u003cp\u003eMr. Ayush Adhikari conceived the study, acquired, analyzed, and interpreted data. Ms. Rubina drafted the manuscript and Mr. George contributed in data tabulation, presentation and final editing. Author has read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eThe author would like to express gratitude to Mr. Bhupal Baniya for supervision and guidance, Prof. Kalpana Tiwari for valuable input, and the management team of Healthy Choice Clinic for providing consent and opportunity to conduct this research.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eObesity. and overweight [Internet]. [cited 2026 Mar 8]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight\u003c/span\u003e\u003cspan address=\"https://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDespr\u0026eacute;s JP, Lemieux I. 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BMC Public Health. 2018;18:677. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12889-018-5600-9\u003c/span\u003e\u003cspan address=\"10.1186/s12889-018-5600-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PubMed PMID: 29855293.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThompson PD, Crouse SF, Goodpaster B, Kelley D, Moyna N, Pescatello L. The acute versus the chronic response to exercise. Med Sci Sports Exerc. 2001;33(6 Suppl):S438-445; discussion S452-453. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/00005768-200106001-00012\u003c/span\u003e\u003cspan address=\"10.1097/00005768-200106001-00012\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e PubMed PMID: 11427768.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-nutrition","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nutn","sideBox":"Learn more about [BMC Nutrition](http://bmcnutr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nutn/default.aspx","title":"BMC Nutrition","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Body fat percentage, Cholesterol, Bioelectrical impedance analysis, Lipid profile, Obesity, Cardiovascular risk, Nepal","lastPublishedDoi":"10.21203/rs.3.rs-9256504/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9256504/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe study of body composition and its correlation with lipid profiles are significant markers for the cardiovascular risk factors of the urban Nepali population. This study is an attempt to evaluate the percentage of body fat and determine the parameters that influence the cholesterol levels in the patients attending at Healthy Choice Clinic, Kathmandu, Nepal.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA quantitative cross-sectional study was conducted among 106 adult patients of Healthy Choice Clinic aged 18 years and above. Their socio-demographic profile, lifestyle and dietary practice were sourced through structured interview questionnaire. Body Mass Index (BMI), waist-to-hip ratio (WHR), and percentage of body fat were measured through Bioelectrical Impedance Analysis (BIA). For total cholesterol, triglyceride, HDL, LDL and VLDL assessment, blood sample was taken through laboratory tests. Chi-square and fisher\u0026rsquo;s exact test were used for correlation.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe statistical prevalence showed that the overweight and obese respondent was 51.9%, and 21.7% obese. In addition, about 86.8% respondents had significant abdominal obesity with high waist to hip ratio (WHR). Of which, alcohol intake significantly correlated with high total cholesterol level among other respondents (p\u0026thinsp;=\u0026thinsp;0.044). The body mass index (BMl) was significant with total cholesterol level (p\u0026thinsp;=\u0026thinsp;0.022). The body mass index (BMI), WHR, and body fat percentage were all significantly correlated with high triglycerides (p\u0026thinsp;=\u0026thinsp;0.019, p\u0026thinsp;=\u0026thinsp;0.004, p\u0026thinsp;=\u0026thinsp;0.011; respectively). Regular exercise significantly correlates with reduced very-low-density lipoprotein (VLDL). No significant association was reported between cholesterol and fat percentage and WHR and most of the dietary variables.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eAlthough we could not find a strong correlation between body fat percentage (BFP) and cholesterol levels, BMI and central obesity indicators can provide valuable correlation with CV risk relating to dyslipidemia. Physical activity is shown to have a protective effect against dyslipidemia. These results can support the significance of a detailed clinical assessment with anthropometric measurements and lifestyle history to identify and manage patients with risk factors for CV disease.\u003c/p\u003e","manuscriptTitle":"Body Fat Percentage and Factors Associated with Cholesterol Levels among Patients at Healthy Choice Clinic in Kathmandu, Nepal: A Cross-Sectional Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-23 12:28:21","doi":"10.21203/rs.3.rs-9256504/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-07T03:24:43+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-04T07:27:12+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-22T18:28:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"200964438402779800384844381374462166855","date":"2026-04-19T13:17:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"212138173417393121260466155684813322176","date":"2026-04-18T09:56:59+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-15T15:59:06+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-15T15:55:38+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-10T04:18:35+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-09T14:58:46+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Nutrition","date":"2026-04-09T13:51:54+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-nutrition","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nutn","sideBox":"Learn more about [BMC Nutrition](http://bmcnutr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nutn/default.aspx","title":"BMC Nutrition","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"fd4958bf-db73-4ac1-a9df-47ed709aa9b6","owner":[],"postedDate":"April 23rd, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-07T03:24:43+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-04T07:27:12+00:00","index":33,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-07T03:38:35+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-23 12:28:21","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9256504","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9256504","identity":"rs-9256504","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}