Prevalence and Clinical Correlates of Overweight and Obesity among Staff of Usmanu Danfodiyo University, Sokoto: A Cross-Sectional Study

IRASS Journal of Applied Medical and Pharmaceutical Sciences https://irasspublisher.com/journal-details/IRASSJAMPS ISSN (Online) 3049-0901 This is an open access article under the CC BY-NC license 19 JOURNAL COVER PAGE Prevalence and Clinical Correlates of Overweight and Obesity among Staff of Usmanu Danfodiyo University, Sokoto: A Cross-Sectional Study Hamidat Oluwatoyin Ajiboye1, Basiru Shuaibu1*,Msheliza Saminu2, Oyeyiola Taofiq Tunde3 *1Department of Physiology, Faculty of Basic Medical Sciences, College of Health Sciences, Usmanu Danfodiyo University, P.M.B. 2346, Sokoto, Nigeria 1Department of Health Services, Usmanu Danfodiyo University, P.M.B. 2346, Sokoto, Nigeria 2 The Leprosy Mission Nigeria (TLMN), Federal Capital Territory, Abuja, Nigeria 3Anchormed Hospital, Niyi Aniyikaye Street, Tanke, Ilorin Kwara State, Nigeria Corresponding Author Basiru Shuaibu Department of Physiology, Faculty of Basic Medical Sciences, College of Health Sciences, Usmanu Danfodiyo University, P.M.B. 2346, Sokoto, Nigeria. Article History Received: 18 / 08 / 2025 Accepted: 14 / 11 / 2025 Published: 22 / 11 / 2025 Abstract: Overweight and obesity, disorders of energy metabolism characterized by excess adipose tissue, contribute significantly to global morbidity and mortality. With increasing urbanization and lifestyle shifts, Nigeria faces rising prevalence rates, including among university staff. The study aimed to determine the prevalence and clinical correlates of overweight and obesity among staff of Usmanu Danfodiyo University, Sokoto (UDUS). Descriptive cross-sectional study was used among 109 consenting non-academic and academic staff of UDUS using structured questionnaires to collect socio-demographic and behavioral data. Anthropometric measurements and biochemical assays, including fasting blood glucose, lipid profiles, leptin, and renal function tests, were performed. Data were analyzed by using a logistic regression to identify determinants of overweight and obese, with statistical significance of p < 0.05. The prevalence of overweight and obesity combined were 47%, with non-academic staff showing higher risk. Family history of diabetes and medication use was significant which were associated overweight and obesity (p < 0.05). Other clinical markers such as hypertension history, lipid parameters, fasting glucose, and leptin levels did not show significant associations. Occupation and fruit consumption frequency independently predicted overweight and obesity after adjusting for age. The high prevalence aligns with prior regional studies and underscores the impact of sedentary work and dietary habits on obesity risk. The paradoxical positive association between fruit consumption and obesity may reflect intake of sugar-rich fruit products. Overweight and obesity remain highly prevalent among UDUS staff, influenced by occupational status and dietary patterns. Workplace health interventions focusing on activity promotion and nutrition education are essential. Keywords: Prevalence, overweight, Obesity, UDUS, clinical correlate. How to Cite: Ajiboye, H. O., Shuaibu, B., Saminu, M. & Tunde, O. T. (2025). Prevalence and Clinical Correlates of Overweight and Obesity among Staff of Usmanu Danfodiyo University, Sokoto: A Cross-Sectional Study. IRASS Journal of Applied Medical and Pharmaceutical Sciences, 2(11), 19-24. Introduction The imbalance between energy intake and expenditure such that excess energy is stored in fat cells is referred to as overweight and obesity (Heymsfield & Wadden, 2017). These are the disorders of energy metabolism involving excess adipose tissues stored in the body and can be associated with medical and psychological morbidities (Heymsfield & Wadden, 2017). The 5th leading risks of death worldly is the overweight and obesity with at least 2.8 million adults’ deaths occurring annually from complications of overweight and obesity. According to World Health Organization (WHO) about 1.9 billion of the world population is overweight while 650 million are obese as of 2016. Moreover, about 44% of diabetes mellitus burden, 23% of ischemic heart diseases, and 7%– 41% of certain cancer burdens are attributable to overweight and obesity (WHO, 2021). In Nigeria, the prevalence of overweight is 27.6%, while that of obesity is 14.5% (Ijezie et al., 2022). A study by Hruby and Hu, stated that nutritional transition, sedentary lifestyle, changing methods of transportation and increasing urbanization are fuelling non-communicable diseases (Hruby & Hu, 2015). The poor eating habits including increased in consumption of high energy food, high level of sugar, and saturated fats combined with the physical inactivity have led to raise in prevalence of overweight and obesity in many parts of the world (Kleinendorst et al., 2018). The health risks associated with obesity include coronary heart disease (CHD) and other cardiovascular diseases like atherosclerotic, stroke, high blood pressure (HBP), with the addition of type 2 diabetes mellitus (T2D), kidney disease, sleep apnoea, osteoarthritis, gallstones, fatty liver disease, stress incontinence, and some gynaecological abnormalities (amenorrhea and menorrhagia) as well as various types of cancer (Adaja & Idemudia, 2018). The study of Sabir and his colleagues have reported that obesity and advancing in age are the major risk factors fuelling an raised in the prevalence of type 2 diabetes mellitus among Nigerian population (Ahmad Sabir et al., 2016). The study of Dankyau and his co-researchers reported a high prevalence of overweight and obesity (31.4% and 23.2% respectively) among tertiary hospital workers in North-Central IRASS Journal of Applied Medical and Pharmaceutical Sciences Vol-2, Iss-11 (November2025): 19-24. Vol-2, Iss-11 (November-2025) 20 Nigeria (Dankyau et al., 2016). A high prevalence of increased in blood pressure was also reported by the study of Owolabi and colleagues (Owolabi et al., 2015) among health‑care workers in Nigeria regardless their awareness of the disease. It has also reported that been a well-known fact that high body mass index (BMI) predisposes to certain cancers (Zhu et al., 2005). The problem of high income countries like USA that is 2/3 of the population is obese and overweight and they are now on the increase in low‑ and middle‑income countries, most especially in the urban dwellers (Iwuala et al., 2015). Energy balance is central to living a healthy life. Energy intake and energy expenditure are therefore needed to be balanced and are therefore the major mechanisms by which energy balance is obtained (Gadde et al., 2018). Method of the study Study location The study was conducted in Sokoto, the capital city of Sokoto State, which is located in Nigeria's North-western region of the country and has a land area of 26,648.48 square kilometres. According to the 2006 National Population Census, Sokoto state has the population of 3.09 million people, with a yearly growth rate of 3.0%. Usmanu Danfodiyo University Sokoto (UDUS) is a research university in Sokoto, Nigeria's northwest region. The university has many faculties spread across the Main Campus (permanent site), City Campus Complex (consisting of College of Health Sciences located in UDUTH and temporary site, also known as City Campus), and has a workforce of approximately 5000 people in both academic and non-academic divisions. Ethical consideration The ethical consideration was obtained from Usmanu Danfodiyo University Teaching Hospital (UDUTH), Sokoto Ethical Committees and granted permission to conduct this research under the registration number NHREC/30/012/2019. Design of the study We used cross-sectional descriptive study in this our study. Population The study included men and women that is working at UDUS, including both academic and non-academic members of the institution's staff. Criteria for Participants’ Selection (a) Inclusion criteria In the study, individuals who appeared to be healthy and permanent university employees were recruited for this study. (b) Exclusion criteria Some subjects were excluded from this study which include: 1. Non-consenting individuals 2. Severely ill staff 3. Pregnant women Informed Consent A participant information slip and a standard informed consent form were distributed to the target population. A structured questionnaire was given to all consenting members of staff for their socio-demographic information, followed by anthropometric measures of weight, height, as well as waist circumference. Sample calculation of the study The sample size used in this study was 109 which was determined according to Cochran, (1999) using a population of 10000 using the formula below: n = z2 PQ/d2 (Sharma et al., 2020) Where: n = minimum sample size z = standard normal deviation and probability i.e., 0.05 at 95% confidence interval (1.96). P = prevalence (20-29.3%), average = 26.5% (Ijezie et al., 2022) q = 1-p d = tolerance limit (0.08) n = (1.96)2 x (0.265) (10.265)/ (0.083)2 0.7482/0.006889 108.6 n = 109 Sampling Technique Multi stages (probability sampling technique) was used. Faculties in both Permanent Site and City Campus Complex of the University were involved in the research including Faculty of Agriculture, Arts and Islamic Studies, Basic Medical Sciences, Basic Clinical Sciences, Clinical Sciences, Dental Sciences, Engineering and Environmental Design, Education and Extension Services, Law, Management Sciences, Pharmaceutical Sciences, Sciences, Social Sciences, and Faculty of Veterinary Medicine. Data collection The questionnaire used in this study included items such as gender, age, occupational grade level, marital status, cigarette smoking status, alcohol intake, current treatment history, family history of obesity, family history of hypertension and diabetes mellitus, dietary patterns, hours of sleep as well as physical activity. The level of education was defined as the highest-grade level attained in school and participants were categorized as Senior Secondary Certificate (SSCE)/Diploma and those with a tertiary education. Participants were categorized based on their grade level into senior and junior staff. Weight and height were recorded using a weight scale and meter rule in line with WHO protocols (WHO, 1995). In addition waist Circumference was also measured in line with WHO protocol using a non-stretch tape measure (WHO, 2018). Blood Sample collection A 5ml disposable syringe was used to collect 5 ml of blood from each patient's cubital vein in the arm. One ml of the blood was quickly transferred into anticoagulant free (plain) bottles for fasting blood sugar (FBS), and the remaining 4mls were transferred into plain tubes. The serum was separated after the sample in the plain tubes clotted and was centrifuged. The total cholesterol, triglycerides, the high-density lipoprotein (HDL), the low-density IRASS Journal of Applied Medical and Pharmaceutical Sciences Vol-2, Iss-11 (November2025): 19-24. Vol-2, Iss-11 (November-2025) 21 lipoprotein (LDL), glomerular filtration rate (GFR), and leptin levels were all determined using serum samples. Biochemical Analyses was carried out from the Department of Chemical pathology, UDUS. Data analysis The data was analysed using the SPSS® version 25 (IBM Corp, Armonk, NY, USA). The categorical variables have their data presented as both frequency and proportions (%). Determinants of overweight and obesity were assessed by computing odds ratios (ORs) using univariate logistic regression, multivariate ORs and their 95% confidence intervals (95% CIs) using multivariate analysis of regression. A logistic regression model analysis, with age as a covariate, was utilised to determine the individual attributes that contribute to overweight and obesity. In this study, the statistical significance was considered as a pvalue of less than 0.05. Results Sociodemographic information of the Respondents The total number of 114 questionnaire was administered to the participants, however, only 109 submitted and found suitable for analysis. Thus, giving a response rate of 95.6%. A total of 109 respondents consisting of 85 (78.0%) male and 24 (22.0%) of the female were recruited for the study. Majority of the respondents were Hausa/Fulani 73 (67.0 %) by tribe, majority had tertiary education (89.9%), 85 (78.0%) were married, 91 (83.5%) were Muslims, 101 (92.7%) lived in urban area (92.7%), 65 (59.6) were non-academic staff, 50 (45.9%) had above average monthly salary, and 89 (81.7) used motorcar as means of transport. Many of the participants preferred to eat breakfast 55 (50.5%) other than snacks and fried foods, 73 (67.0%) sometimes eat sweet and chocolate, and beverages 64 (58.7%). Moreover, there is no significant difference (p = 0.966) among the participants that watch television for at least 2 hours 53 (48.6) or more 44 (40.4) per week. Meanwhile, the majority of the respondents had no history of hypertension (60.6%), obesity (86.2%), diabetes (68.8%), and were not on any form of medication (69.7%). Table 1. Simple Logistics Regression (Bivariate) Analysis of the Medical Factors as Determinants of Overweight and Obesity among staff of UDUS Variable Crude OR (95% CI) Wald Statistics (df) P-value* History of Hypertension None Yes (Father only) Yes (Mother only) Both Parents 0.761 (0.546, 1.059) 2.263 (1) 0.105 History of Obesity None Yes (Father only) Yes (Mother only) Both Parents 0.606 (0.322, 1.139) 2.423 (1) 0.120 History of Diabetes None Yes (Father only) Yes (Mother only) Both Parents 0.651 (0.427, 0.993) 3.979 (1) 0.046 Medication History None Antidiabetics Antihypertension Others 0.674 (0.436, 0.961) 4.650 (1) 0.031 IRASS Journal of Applied Medical and Pharmaceutical Sciences Vol-2, Iss-11 (November2025): 19-24. Vol-2, Iss-11 (November-2025) 22 Table 2. Simple Logistics Regression (Bivariate) Analysis of the Biochemical Factors as Determinants of Overweight and Obesity among staff of UDUS Variable Crude OR (95% CI) Wald Statistics (df) Pvalue* FBS Normal Abnormal 1.212 (0.567, 2.590) 0.247 (1) 0.619 Total cholesterol Normal Abnormal 0.642 (0.225, 1.832) 0.687 (1) 0.407 Triacyl glycerol Normal Abnormal 2.600 (0.482, 14.022) 1.235 (1) 0.266 HDL Normal Abnormal 1.027 (0.457, 2.310) 0.004 (1) 0.949 LDL Normal Abnormal 1.143 (0.383, 3.406) 0.057 (1) 0.811 Leptin Normal Abnormal 0.000 (0.000, 0) 0.000 (1) 0.998 Serum Creatinine Normal Abnormal 0.714 (0.291, 1.754) 0.539 (1) 0.463 Factors Associated with Obesity among Staff of UDUS From multivariate regression analysis, after the adjustment for age of the respondents, only occupation and number of fruits eaten per week were observed to be statistically significant and independent determinants of overweight and obesity among staff of the UDUS (Table 3). Table 3. Multiple Logistics Regression (Multivariate) Analysis on Determinants of Overweight and Obesity among Staff of UDUS Variable Crude OR a (95% CI) Adjusted OR b (95% CI) Wald Statistics b (df) p value b Occupation Academic staff Non-academic staff 0.522 (0.266, 1.022) 6.281 (0.259, 149.266) 7.314 (2) 0.026 Fruits/week None Yes Lee than 3 times More than 3 times 0.672 (0.438, 1.031) 1.529 (34.632, 784.396) 14.105 (3) 0.003 a Simple Logistics Regression, b Multiple Logistics Regression, P<.05 Discussion Our present study identified a high prevalence of overweight as well as obesity among UDUS staff, consistent with prior reports in similar populations. Nearly half (47%) of staff were either overweight or obese, aligning closely with earlier studies by Nkwoka and his colleagues (2014), that reported prevalence of 47% among UDUS employees a decade ago. Comparable trends of increasing overweight and obesity have been documented in Nigerian university staff and urban adult populations, reflecting rapid urbanization, lifestyle changes, and socioeconomic factors (Adetunji, 2019; Nkwoka et al., 2014; Olowookere et al., 2018). The elevated rates among non-academic staff support the recognized association between sedentary occupational roles and increased adiposity risk (Hanna et al., 2019). The analysis revealed that the family history regarding diabetes and medication history were statistically significant related with overweight and obesity, consistent with the shared genetic and metabolic pathways influencing obesity and its comorbidities (Ng et al., 2014). However, other clinical markers including history of hypertension and obesity showed no significant association, which contrasts with general IRASS Journal of Applied Medical and Pharmaceutical Sciences Vol-2, Iss-11 (November2025): 19-24. Vol-2, Iss-11 (November-2025) 23 epidemiological trends where obesity closely correlates with hypertension and cardiovascular risk (Lean et al., 2018; World Health Organization, 2020). Lipid profile components, fasting blood glucose, and serum leptin levels were also not significantly associated in this study, which may be attributed to sample size, specific population characteristics, or measurement factors (Ng et al., 2014). Multivariate analysis showed that occupation and frequency of fruit consumption independently predicted overweight and obesity after adjustment for age. Non-academic staff are most likely to be overweight or obese than academic staff, supporting prior findings linking sedentary work environments with obesity risk (Tudor-Locke et al., 2015). The positive association of fruit consumption and obesity is noteworthy and paradoxical; it mirrors evidence that while whole fruits are protective against obesity, consumption of fruit juices or sugar-rich fruit products may contribute to increased caloric intake and weight gain via high monosaccharide content and hepatic lipogenesis (Bray & Popkin, 2014; Shefferly et al., 2016). Conclusion The present study highlights a persistently increased prevalence of overweight and obesity among staff of UDUS, affecting nearly half of the population surveyed. Our findings is consistent with some previous research with similar settings, underscoring the public health challenge posed by obesity in this group. The association of obesity with family history of diabetes and medication use reinforces known genetic and metabolic connections, although some expected clinical markers showed no significant links, which may reflect population or methodological nuances. The study further identifies occupational status and fruit consumption frequency as independent determinants, with sedentary non-academic roles and paradoxically higher fruit intake correlating with greater obesity risk. These insights emphasize the complex interplay of socio-demographic, behavioral, and clinical factors in obesity among university staff. Addressing these factors through targeted workplace health programs and nutrition education could mitigate obesity’s impact. Future research should investigate the biochemical pathways and lifestyle dynamics in this population to better inform intervention strategies. References 1. Adaja, T. M., & Idemudia, O. J. (2018). Prevalence of overweight and obesity among health-care workers in University of Benin Teaching Hospital, Benin City, Nigeria. Annals of Tropical Pathology, 9, 150–154. https://doi.org/10.4103/atp_30_18 2. Ahmad Sabir, A., Jimoh, A., Omozehio Iwuala, S., Alabi Isezuo, S., Suleiman Bilbis, L., Umar Aminu, K., Atta Abubakar, S., & Saidu, Y. (2016). Metabolic syndrome in urban city of North-Western Nigeria: prevalence and determinants. Pan African Medical Journal, 1–7. https://doi.org/10.11604/pamj.2016.23.19.5806 3. Bray, G. A., & Popkin, B. M. (2014). Dietary sugar and body weight: Have we reached a crisis in the epidemic of obesity and diabetes? Health Affairs, 33(10), 1643–1650. https://doi.org/10.1377/hlthaff.2014.0458 4. Dankyau, M., Shu′aibu, J., Oyebanji, A., & Mamven, O. (2016). Prevalence and correlates of obesity and overweight in healthcare workers at a tertiary hospital. Journal of Medicine in the Tropics, 18(2), 55. https://doi.org/10.4103/2276-7096.188533 5. Gadde, K. M., Martin, C. K., Berthoud, H.-R., & Heymsfield, S. B. (2018). The Present and Future Stateof-the-Art Review; Obesity Pathophysiology and Management. Journal of the American College of Cardiology, 71(1), 69–84. https://doi.org/10.1016/j.jacc.2017.11.011 6. Haddad, L., Hawkes, C., Waage, J., Webb, P., & Godfray, C. (2016). Food systems and diets: Facing the challenges of the 21st century. https://openaccess.city.ac.uk/id/eprint/19323/ 7. Hanna, F., Daas, R. N., El-Shareif, T. J., Al-Marridi, H. H., Al-Rojoub, Z. M., & Adegboye, O. A. (2019). The relationship between sedentary behavior, back pain, and psychosocial correlates among university employees. Frontiers in Public Health, 7(APR), 80. https://doi.org/10.3389/fpubh.2019.00080 8. Hruby, A., & Hu, F. B. (2015). The Epidemiology of Obesity: A Big Picture. In PharmacoEconomics (Vol. 33, Issue 7). https://doi.org/10.1007/s40273-014-0243-x 9. Ijezie, I., Id, C., Arinze, K., Id, O., Ogahid, O. S., Johnid, C., Oviasu, E., Anyabolu, E. N., Ezeani, I. U., Uche, G., Iloh, P., Chukwuonye, E., Raphaelid, C. O., Onwuchekwa, U., Hughes Okafor, U., Oladele, C., Chukwuebuka Obi, E., Godswill Okwuonu, C., Iheji, O., … Okpechiid, I. G. (2022). Prevalence of overweight and obesity in Nigeria: Systematic review and meta-analysis of population-based studies. PLOS Global Public Health, 2(6), e0000515. https://doi.org/10.1371/JOURNAL.PGPH.0000515 10. Iwuala, S. O., Ayankogbe, O. O., Olatona, F. A., Olamoyegun, M. A., OkparaIgwe, U., Sabir, A. A., & Fasanmade, O. A. (2015). Obesity among health service providers in Nigeria: danger to long term health worker retention? The Pan African Medical Journal, 22, 1. https://doi.org/10.11604/pamj.2015.22.1.5586 11. Kleinendorst, L., & et al. (2018). Genetic obesity: nextgeneration sequencing results of 1230 patients with obesity. Journal of Medical Genetics, 55(9), 578–86. 12. Lean, M. E. J., Han, T. S., & Seidell, J. C. (2018). Obesity and cardiovascular disease: Risk factor, paradox, and impact of weight loss. European Heart Journal, 39(15), 1122–1130. https://doi.org/10.1093/eurheartj/ehx019 13. Ng, M., Fleming, T., Robinson, M., Thomson, B., Graetz, N., Margono, C., & Gakidou, E. (2014). Global, regional, and national prevalence of overweight and obesity in children and adults during 1980–2013: A systematic analysis for the Global Burden of Disease Study 2013. The Lancet, 384(9945), 766–781. https://doi.org/10.1016/S0140-6736(14)60460-8 14. Nkwoka, I., Egua, M. O., Abdullahi, M., Sabi’u, A., & Mohammed, A. I. (2014). Overweight and obesity among staff of Usmanu Danfodiyo University, Sokoto, Nigeria. Undefined. 15. Nkwoka, S. C., Umar, A., & Sani, M. I. (2014). Prevalence of overweight and obesity among staff of Usmanu Danfodiyo University, Sokoto. Journal of Epidemiological Research, 6(2), 112–119. IRASS Journal of Applied Medical and Pharmaceutical Sciences Vol-2, Iss-11 (November2025): 19-24. Vol-2, Iss-11 (November-2025) 24 16. Olowookere, S. A., Odunaiya, N. A., & Ismail, S. M. (2018). Obesity trends and associated factors among university workers in Nigeria. Nigerian Medical Journal, 59(1), 12–18. https://doi.org/10.4103/nmj.NMJ_44_18 17. Owolabi, A., Owolabi, M., OlaOlorun, A., & Amole, I. (2015). Hypertension prevalence and awareness among a health workforce in Nigeria. Internet Journal of Medical Update - EJOURNAL, 10(2), 10–19. https://doi.org/10.4314/ijmu.v10i2.3 18. Sharma, S., Mudgal, S., Thakur, K., & Gaur, R. (2020). How to calculate sample size for observational and experiential nursing research studies? National Journal of Physiology, Pharmacy and Pharmacology, 10(1), 1–8. https://doi.org/10.5455/njppp.2020.10.0930717102019 19. Shefferly, A., Hurwitz, M., & Skinner, J. (2016). Longitudinal association between fruit juice consumption and BMI change among U.S. children. Pediatric Obesity, 11(4), 340–346. https://doi.org/10.1111/ijpo.12002 20. Tudor-Locke, C., Schuna, J. M., & Katzmarzyk, P. T. (2015). Sedentary behavior and chronic disease risk: Implications for health promotion. Preventive Medicine, 76, 108–114. https://doi.org/10.1016/j.ypmed.2015.03.023 21. WHO. (1995). Physical status: the use and interpretation of anthropometry. Report of a WHO Expert Committee. In World Health Organization technical report series (Vol. 854). https://doi.org/10.1002/(sici)15206300(1996)8:6<786::aid-ajhb11>3.0.co;2-i 22. WHO. (2018). WHO | Waist circumference and waist– hip ratio. WHO. https://www.google.com/url?sa=t&source=web&rct=j&u rl=https://www.who.int/nutrition/publications/obesity/W HO_report_waistcircumference_and_waisthip_ratio/en/ &ved=2ahUKEwjeg5K_m9bmAhVbThUIHXnOBb4QFj AAegQIAhAB&usg=AOvVaw0oJSEV5u8hRol28jLkBHa 23. WHO. (2021). Obesity and overweight. https://www.who.int/news-room/factsheets/detail/obesity-and-overweight 24. World Health Organization. (2020). Obesity and overweight. https://www.who.int/news-room/factsheets/detail/obesity-and-overweight 25. Zhu, K., Caulfield, J., Hunter, S., Roland, C. L., PayneWilks, K., & Texter, L. (2005). Body mass index and breast cancer risk in African American women. Annals of Epidemiology, 15(2), 123–128. https://doi.org/10.1016/j.annepidem.2004.05.011