The prevalence of pre-diabetes and diabetes in people with and without past history of COVID-19 in northeastern Nigeria

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The burden of COVID-related DM has not been described in Africa, where the overall risk of DM has been increasing rapidly. Our objective was to compare the prevalence of pre-DM and DM in Nigerian individuals with a history of COVID-19 to individuals without known COVID-19 infection. Methods We identified 256 individuals with a past medical history of COVID-19 with no history of pre-DM or DM and 256 individuals without a history of COVID-19 or pre-DM/DM. Participants were categorized as pre-DM (fasting capillary glucose 100–125 mg/dL) or DM (fasting capillary glucose ≥ 126 mg/dL). We used multivariate multinomial logistic regression to determine the odds of pre-DM and DM in those with and without a history of COVID-19 after adjustment for age, gender, the presence of hypertension, physical activity, central adiposity, and family history of DM. Results Compared to the control group, those with a history of COVID-19 had a similar median age (38 vs 40 years, p = 0.84), had a higher proportion of men (63% vs 49%), and had a lower prevalence of central adiposity (waist: hip ratio ≥ 0.90 for males and WHR ≥ 0.85 for females) (48% vs 56.3%, p = 0.06). Of the 256 with a history of COVID-19, 44 (17%) required inpatient care. The median (interquartile range) time interval between COVID-19 diagnosis and the glycaemic assessment was 19 ( 14 , 24 ) months. Pre-DM prevalence was 27% in the post-COVID-19 group and 4% in the control group, whereas the prevalence of DM was 7% in the post-COVID-19 group and 2% in the control group. After multivariable adjustment, the odds of pre-DM were 8.12 (95% confidence interval (CI): 33.98, 16.58; p < 0.001) higher, and the odds of DM were 3.97 (95% CI: 1.16, 13.63) higher in those with a history of COVID-19 compared to controls. Conclusion Previous COVID-19 was found to be a risk factor for prevalent pre-diabetes and diabetes mellitus in Nigeria. More intensive screening for DM in those with a history of COVID-19 should be considered. COVID-19 Diabetes Mellitus Nigeria Glycaemic Assessment Risk Factors Figures Figure 1 Figure 2 Introduction In addition to pneumonia, COVID-19 is associated with multi-systemic complications, such as coagulopathy, myocarditis, and acute kidney injury( 1 ). New onset diabetes mellitus (DM) after COVID-19 has also been reported worldwide, predominantly in Asia, Europe and the US ( 2 , 3 ) In a systematic review and metanalysis conducted in 2022, it was reported that males who had contracted COVID-19 had a two-fold risk of developing DM when compared to those without a past history of COVID-19 ( 2 ). Furthermore, in a related study, the incidence of diabetes post-COVID-19 was estimated to be 15.53 per 1000 person-years ( 4 ). Moreover, the risk of developing type 2 diabetes following COVID-19 was significantly higher, with a relative risk (RR) of 1.70 (95% confidence interval (CI):1.32–2.19), when compared to individuals without a past history of COVID-19 ( 4 ). The etiological mechanisms contributing to post-COVID-19 DM have not been fully elucidated. Potential mechanisms include a direct impact on pancreatic beta cells, which may compromise insulin secretion ( 4 – 6 ). Other factors, such as steroid therapy in the management of COVID-19 infection, have also been implicated in the pathogenesis of post-COVID-19 diabetes ( 3 , 6 – 15 ). Although the impact of COVID-19 pandemic in Africa has not been as pronounced as in other parts of the world, the continent has experienced significant infection rates, resulting in near-term and long-term morbidity and mortality. The data regarding the impact of COVID-19 on DM within the African context is notably limited. The ongoing COVID-19 pandemic may add to the rising burden of DM in Africa, which is already at an epidemic level( 9 ). This study retrospectively examines the comparative prevalence of diabetes and pre-diabetes in individuals with and without a past history of COVID-19, thereby investigating the evolving hypothesis that there is an epidemiological link between COVID-19 and abnormal glucose metabolism. Methods Study Population Persons with a history of COVID-19 were identified through ( 1 ) the hospital admission database of the state’s epidemiology unit, Ministry of Health, ( 2 ) referrals from inpatient care teams for those hospitalized with COVID-19, and 3) identification by the local disease surveillance notification officers (DSNOs) across two States in northeastern Nigeria (Adamawa and Gombe). The DSNOs tracked individuals into their various communities and enlisted them after obtaining informed consent. The diagnosis of COVID-19 was previously established in this cohort using the polymerase chain reaction (PCR) for hospitalized patients and urban dwellers, while the rapid antigen test kits were used for non-hospitalized patients and rural dwellers. We excluded those below 18 or over 75 years, pregnant women and those who self-reported having diabetes or prediabetes prior to the COVID-19 pandemic. Individuals without a past history of COVID-19 and did not have a self-reported history of diabetes or prediabetes served as a control group. We recruited participants during a free medical outreach organized in the community, during which free blood sugar and blood pressure were checked. Those who self-reported having diabetes or prediabetes were excluded. Additional control participants were recruited when they presented to the healthcare facility as an outpatient to the general outpatient department or as a patient’s relative. Ethical approval was obtained from the health research committee of the Modibbo Adama University Teaching Hospital Yola Adamawa State, Nigeria MAUTH, with approval number MAUTHYOLA/HREC/22/237. All participants gave informed consent. Data collection Data were collected from December 2022 to April 2023. Trained DSNOs administered the questionnaire by tracking the COVID-19-positive cohort in their communities. A trained nurse enrolled healthcare workers who met the eligibility criteria in their various healthcare facilities. We collected data on sociodemographic information, behavioural risk factors for DM, personal history of hypertension, and family history of hypertension and DM. Glucose was tested using capillary blood (Accucheck) in participants after 8 hours of fast, and their waist-hip ratio was taken. Blood pressure was measured by adhering to the WHO standardized procedure. Blood pressure was assessed with two separate measurements 30 minutes apart after resting for at least 5 minutes in accordance with WHO standardized procedures( 17 ). Waist circumference was measured using a flexible tape at the level of the midpoint between the ribs and the iliac crest from the front after exhalation( 16 ). Hip circumference was measured using a flexible tape at the point where the buttocks extend the most when viewed from the side( 16 ). We targeted a sample size of 256 in each group to detect a difference in diabetes prevalence with an alpha of 0.05 with a power of 80%, assuming a diabetes prevalence in the COVID-19 group of 20.8% ( 19 ). Study outcome The primary outcome was the prevalence of diabetes and pre-diabetes, defined using criteria established by the American Diabetes Association.( 4 ) Diabetes was defined as a fasting glucose concentration ≥ 7.0mmol/L and pre-diabetes was defined as a fasting glucose concentration of 5.6–6.9 mmol/l. Capillary blood was used for glycaemic status assessment( 18 ) Covariates Hypertension was defined as either a systolic blood pressure ≥ 140 or diastolic ≥ 90 or both or self-reported hypertension on treatment. Central obesity was defined using the adult waist-hip ratio (WHR), male WHR ≥ 0.90 and female WHR ≥ 0.85( 19 ). Physical activity was measured using World Health Organization criteria for physical inactivity( 20 ). Physical inactivity was defined as the failure to meet WHO recommendations on physical activity for health, which are defined as engaging in at least 150 minutes of moderate-intensity activity per week or 75 minutes of vigorous-intensity activity per week, through any combination of walking and moderate or vigorous-intensity activities. Statistical analysis All statistical analyses were performed using Stata software, version 17.0 and R version 4.0.3. Data were presented as frequencies and percentages for categorical variables. The prevalence of diabetes and pre-diabetes with Clopper-Pearson 95% confidence interval was presented for individuals with and without a history of COVID-19. We assessed the relationship between those with and without a history of COVID-19 and diabetes (normal blood glucose vs. pre-diabetic and diabetic) using multinomial logistic regression. Normal blood glucose was used as the reference. All models were adjusted for age (=50), gender (male vs. female), hypertension (yes vs. no), family history of DM (yes vs. no), physical activity (active vs. sedentary) and waist-hip-ratio (normal vs. central obesity). RESULTS Figure 1 describes the flowchart for participant recruitment. Initially, 294 potential post-COVID participants were identified; 31 with self-reported diabetes were excluded, as were seven who declined consent, leaving 256 participants that were included in this study. Of these, 44 were hospitalized, with 16 testing their baseline blood sugar levels at diagnosis; 4 of these developed new-onset diabetes, while the rest were euglycemic. The remaining 240 did not have baseline blood sugar checked. For the control cohort, 300 potential participants were identified, but 44 with self-reported diabetes were excluded. Participant characteristics for the two groups are given in Table 1 . Those with a history of COVID-19 were more likely to be male than those without a history of COVID-19 (62.5% vs 49.4%. p = 0.002 ). Significant differences in marital status between the two groups were observed. For instance, there were nearly twice as many single individuals in the COVID-19 cohort compared to the control group (26.5% vs. 14.5%). Also, while the majority were married in both cohorts, there were slightly more so in the control group (69.9% vs. 76.2%). Those with a history COVID-19 were more likely to have a tertiary education, and less like to have no formal education compared to the control group. The occupations varied significantly between the two groups (P < 0.001), with the COVID-19 cohort having more civil servants (27.3% vs. 8.2%) and healthcare workers (16% vs. 0%). Table 1 Baseline demographic characteristics of study participants Characteristics Positive Past medical history of COVID-19 cohort Control cohort p-value Age, years 39.84 40.74 0.25 Gender Male 160 (62.5%) 126 (49.2%) 0.002 Female 96 (37.5%) 130 (50.8%) Marital Status Divorced 3 (1.2%) 9 (3.5%) 0.001 Married 179 (69.9%) 195 (76.2%) Single 68 (26.5%) 37 (14.5%) Widowed 6 (2.3%) 15 (5.9%) Education No formal education 30 (11.7%) 149 (58.2%) < 0.001 Primary school 29 (11.3%) 19 (7.4%) Secondary 96 (37.5%) 54 (21.1%) Tertiary 101 (39.5%) 34 (13.3%) Occupation Artisan 3 (1.2%) 14 (5.5%) < 0.001 Civil Servant 70 (27.3%) 21 (8.2%) Farmer 61 (23.8%) 93 (36%) Full time housewife 21 (8.2%) 36 (14.1%) Healthcare Worker 41 (16%) 0 (0%) None 1 (0.4%) 14 (5.5%) Student 30 (11.7%) 5 (2%) Trader/Business 25 (9.8%) 57 (22.3%) Others 3 (1.2%) 16 (6.3%) Blood pressure 0.059 Hypertensive 68 (26.6%) 50 (19.5%) Normotensive 188 (73.4%) 206 (80.5%) Fam Hx of NCD < 0.001 Diabetes 5 (2.0%) 7 (3%) Diabetes/ Hypertension 15 (5.9%) 2 (1%) Hypertension 103 (40.2%) 27 (11%) Uknown 27 (10.5%) 18 (7%) None 106 (41.4%) 202 (79%) Physical Activity 0.658 Active 135 (53%) 130 (50.8%) Sedentary 121 (47%) 126 (49.2%) Admission (weeks) Hospitalized 44 (17%) Not-Hospitalized 212 (83%) New onset in-hospital DM No 252 (98%) Yes 4 (2%) Steroid use No 240 (94%) Yes 16 (6%) Waist-Hip-ratio 0.063 central obesity 123 (48%) 144 (56.3%) normal build 133 (52%) 112 (43.8%) Glycaemic status 0.268 Diabetic 12 (4.7%) 4 (1.6%) Prediabetes 68 (26.6%) 11 (4.3%) Euglycemic 176 (68.8) 241 (94.1%) The prevalence of diabetes in the COVID-19 and the control groups were 4.6% (0.046, 95% CI: 0.024, 0.08)) and 1.6% (0.016, 95% CI: 0.004, 0.039), respectively (Fig. 2 ). The prevalence of pre-diabetes in the COVID-19 and the control cohorts were 26.6% and 4.3%, respectively. Results from univariate and multivariable logistic regression models for the associated risk factors of hyperglycaemia (diabetes or pre-diabetes) (Supplementary Table S1A-B) show a substantially increased likelihood of hyperglycaemia (fasting glucose ≥ 100 mg/dL) among individuals with a past COVID-19 infection compared to those without such a history, in both univariate and multivariate models (OR = 7.30, 95% CI: 4.07, 13.11; aOR = 6.58, 95% CI: 3.54, 12.21). Furthermore, while hypertension (OR = 1.95, 95% CI: 1.20, 3.18), and physical inactivity (OR = 2.36, 95% CI: 1.48, 3.75) were significant risk factors for hyperglycemia in univariate models, only physical inactivity (aOR = 2.58, 95% CI: 1.51, 4.41) was a significant risk factors for hyperglycemia after adjusting for other variables in the model. Age, gender, family history of diabetes, and central obesity was not associated with prevalent diabetes in this cohort. In the multivariable multinomial logistic regression models (Table 2 ), the risk of pre-diabetes (vs normoglycemia) was about eight-fold higher in those with a history of COVID-19 (RRR: 7.55, 95% CI: 3.76–15.17, p < 0.0001) compared to those without a history of COVID-19. Similarly, the risk of diabetes (vs normoglycemia) was three-fold higher in those with a history of COVID-19 3.97 (RRR: 3.44, 95% CI: 1.01–11.71, p = 0.028) compared to those without a history of COVID-19. Age and gender were not a significant predictor of having pre-diabetes or diabetes. Hypertension was also not significantly associated with having pre-diabetes but was associated with having diabetes (RRR = 4.63, 95% CI: 1.45, 14.84, p = 0.01). Individuals with low physical activity had higher odds of having pre-diabetes (RRR = 2.87, 95% CI: 1.57, 5.24, p = 0.001) and diabetes (RRR = 4.86, 95% CI: 1.43, 16.56, p = 0.011) compared to active individuals. Results from sensitivity analyses excluding the 16 participants who received steroids during admission for COVID-19 were similar (Supplementary Table S2). Table 2 Multinomial logistic regression of the risk of diabetes and pre-diabetes in individuals with and without a past history of COVID-19 (exclusive of those treated with steroids). Pre-diabetic vs. Normal Diabetic vs. Normal Variable RRR (95% CI) p-value RRR (95% CI) p-value COVID-19 No Yes Ref. 7.55 (3.76, 15.17) < 0.001 Ref. 3.44 (1.01, 11.71) 0.048 Age =50 Ref. 0.68 (0.34, 1.38) 0.291 Ref. 2.04 (0.66, 6.31) 0.216 Gender Male Female Ref. 0.77 (0.43,1.38) 0.377 Ref. 0.54 (0.15, 1.86) 0.326 Hypertension No Yes Ref. 1.45 (0.76, 2.78) 0.261 Ref. 4.23 (1.35, 13.28) 0.014 Family history of DM No Yes Ref. 1.47 (0.63, 3.40) 0.372 Ref. 0.64 (0.07, 5.54) 0.686 Physical activity Active Sedentary Ref. 2.33 (1.31, 4.13) 0.004 Ref. 4.18 (1.24, 14.04) 0.021 Waist-hip-ratio Normal Central obesity Ref. 1.72 (0.93, 3.18) 0.082 Ref. 1.04 (0.33, 3.25) 0.952 Discussion To the best of our knowledge, this is the first study to examine DM and pre-diabetes prevalence in those with and without a history of COVID-19 in Africa, an area that has one of the fastest-growing populations with DM. We showed that the prevalence of DM and pre-DM in persons with a known history of COVID-19 was higher than in those without a history of COVID-19. Our findings suggest that COVID-19 may be a risk factor for pre-diabetes and diabetes in this population. In our study based in Northeastern Nigeria, we found the prevalence of diabetes mellitus to be higher among those with a history of COVID-19 compared to participants without a known history of COVID-19. The observed prevalence in those with a history of COVID-19 was higher than the subregional prevalence of DM of 3.8% (95% CI: 2.7–4.7)( 11 ). Previous studies have also shown a higher risk of diabetes related to COVID-19. In a recent Canadian study, the risk of diabetes in individuals with a history of COVID-19 was 0.5%, as opposed to 0.4% in those without COVID-19 ( 23 ). The study further revealed that COVID-19 accounted for 3.41% of DM in the studied population. In their systematic review and metanalysis, Jiajun et a l ( 24 ), revealed that a history of COVID-19 increased the risk of DM and hyperglycaemia by 1.7 fold compared to those without a history of COVID-19. In the Indian experience, new-onset diabetes among individuals with a history of COVID-19 was observed in 16.7% three months post COVID-19 ( 12 ). Though this study implicated other factors such as age, adiposity, and family history of diabetes in post COVID-19 DM, the observed prevalence is higher than the national prevalence of DM in India of 9.3%( 25 ). At variance with our study, a Chinese-based study reported a 10.3% prevalence of diabetes among COVID-19 patients, which showed almost no difference in the prevalence of 10.9% in the general Chinese population ( 3 , 13 – 14 ). Additionally, Rathmann et al( 26 ) reported a higher incidence of diabetes in those with a history of COVID-19 than in those with other types of common acute viral upper respiratory infections (15.8 vs. 12.3 per 1000 person-years) in Germany( 26 ). The high prevalence of mild COVID-19 could possibly account for the relatively lower prevalence of diabetes in our study. Studies have shown that the incidence risk of post COVID-19 DM increases with the severity of COVID-19 ( 15 , 27 ). According to a systematic review, the global prevalence of pre-diabetes is 5.8% ( 28 ), while the pooled national prevalence of pre-diabetes in Nigeria was estimated at 13.2% ( 29 ). Given that pre-diabetes is a major risk factor for the development of DM, identification of this population may have important public health consequences. It is thought that COVID-19 triggers the pathogenic process of DM by first initiating insulin resistance, then pre-diabetes and finally frank diabetes( 8 ). In addition, other pathophysiologic mechanisms are heightened inflammatory state, disruption of the angiotensin-converting enzyme ACEI/ACE2 balance and subsequent dysfunction of the renin-angiotensin aldosterone-system RAAS ( 30 ). Of note is the direct pancreatic beta cell destruction by the SARS-COV2 ( 30 ). The extent to which this population is at risk of the development of diabetes mellitus deserves further study. The relationship between COVID-19 and diabetes mellitus may be bidirectional ( 5 , 13 , 31 ). Hence, it is possible that the cases of previously undiagnosed diabetes are being recognized during a clinical encounter related to COVID-19. This possibility was suggested by a US-based study which showed that persons with COVID-19 were 40% more likely to be diagnosed with diabetes mellitus compared to those without COVID-19 but were 274% more likely to get diagnosed with DM with the same pre-COVID HbA1c ( 32 ). In our study, although participants with a history of COVID-19 reported no pre-existing diabetes prior to study assessment, it is possible that hyperglycaemia was present at the time of COVID-19 diagnosis but was not clinically apparent. For severe COVID-19, glucocorticoids are indicated for lung disease, which may, in turn, increase the risk of diabetes mellitus( 12 , 33 , 34 ). For example, in an Indian study, of the 31 individuals who had been treated with steroids while on admission for COVID-19, eleven (35.5%) went on to develop new-onset diabetes three months later ( 12 ). Our study results are unlikely to be related to glucocorticoid use. In a sensitivity analysis, excluding those who reported glucocorticoid use as a treatment for COVID-19, the higher odds of prevalent diabetes and pre-diabetes among those with COVID-19 compared to the control group persisted. We acknowledge the following limitations in our study. First, most of our participants did not have a glucose determination at the time of or before their COVID-19 diagnosis. We, therefore, are unable to fully exclude persons with pre-existing diabetes. Second, we used capillary glucose for diabetes determination, which, while suitable for epidemiologic studies, may be slightly different than plasma glucose. An additional assessment with HbA1c could also have been useful to better categorize glycaemic status. Similarly, we did not carry out COVID-19 antibody testing for the control group. Being predominantly retrospective in design, it is not shielded from the inherent recall bias of this design. Lastly, the two cohorts differed on important characteristics, which we attempted to balance with multivariable adjustment. In conclusion, our study suggests that a history of COVID-19 may be a risk factor for diabetes and pre-diabetes in Nigeria. If confirmed in longitudinal studies, more aggressive screening for DM may be warranted among those who have a history of COVID-19. Further studies are also needed to determine the risk of transition to DM in those with a history of COVID-19 with pre-diabetes and what interventions can be implemented to decrease this risk. Declarations Funding None Authors' contributions RIS and JT conceived the project and drafted the manuscript. VH, JS and OA conducted data analysis JT, OA and TTB supervised the methodological approach and interpretation of results. RIS, JT, VH, JS, NU, JO, OA, OUO and TTB contributed to study design, introduction, discussion and critical interpretation of results. JT and RIS led ethics application and project management. All authors critically revised the manuscript for intellectual content. All authors read and approved the final manuscript. Acknowledgement TTB is supported in part by K24 AI120834 Disclosures TTB has served as a consultant to Gilead Sciences, Merck, ViiV Healthcare, and Janssen. Availability of data The dataset supporting the conclusions of this article is included within the article (and its additional files) References White-Dzuro G, Gibson LE, Zazzeron L, White-Dzuro C, Sullivan Z, Diiorio DA, et al. Multisystem effects of COVID-19: a concise review for practitioners. Vol. 133, Postgraduate Medicine. Bellwether Publishing, Ltd.; 2021. p. 20–7. Zhang T, Mei Q, Zhang Z, Walline JH, Liu Y, Zhu H, et al. Risk for newly diagnosed diabetes after COVID-19: a systematic review and meta-analysis. BMC Med. 2022 Dec 1;20(1):444. Shrestha DB, Budhathoki P, Raut S, Adhikari S, Ghimire P, Thapaliya S, et al. New-onset diabetes in COVID-19 and clinical outcomes: A systematic review and meta-analysis. World J Virol. 2021 Sep 25;10(5):275–87. Zhang T, Mei Q, Zhang Z, Walline JH, Liu Y, Zhu H, et al. Risk for newly diagnosed diabetes after COVID-19: a systematic review and meta-analysis. BMC Med. 2022 Dec 1;20(1):444. Lim S, Bae JH, Kwon HS, Nauck MA. COVID-19 and diabetes mellitus: from pathophysiology to clinical management. Vol. 17, Nature Reviews Endocrinology. Nature Research; 2021. p. 11–30. Dallavalasa S, Tulimilli SR V., Prakash J, Ramachandra R, Madhunapantula SR V., Veeranna RP. COVID-19: Diabetes Perspective—Pathophysiology and Management. Vol. 12, Pathogens. MDPI; 2023. Zhang T, Mei Q, Zhang Z, Walline JH, Liu Y, Zhu H, et al. Risk for newly diagnosed diabetes after COVID-19: a systematic review and meta-analysis. BMC Med. 2022 Dec 1;20(1). Lim S, Bae JH, Kwon HS, Nauck MA. COVID-19 and diabetes mellitus: from pathophysiology to clinical management. Vol. 17, Nature Reviews Endocrinology. Nature Research; 2021. p. 11–30. Dessie G, Mulugeta H, Amare D, Negesse A, Wagnew F, Getaneh T, et al. A systematic analysis on prevalence and sub-regional distribution of undiagnosed diabetes mellitus among adults in African countries. Vol. 19, Journal of Diabetes and Metabolic Disorders. Springer Science and Business Media Deutschland GmbH; 2020. p. 1931–41. Li H, Tian S, Chen T, Cui Z, Shi N, Zhong X, et al. Newly diagnosed diabetes is associated with a higher risk of mortality than known diabetes in hospitalized patients with COVID-19. Diabetes Obes Metab. 2020 Oct 1;22(10):1897–906. Uloko Baba M Musa Mansur A Ramalan Ibrahim D Gezawa Fabian H Puepet Ayekame T Uloko Musa M Borodo Kabiru B Sada AE. Prevalence and Risk Factors for Diabetes Mellitus in Nigeria: A Systematic Review and Meta-Analysis. Diabetes Therapy [Internet]. 9. Available from: https://doi.org/10.6084/ Keerthi B, Sushmita G, Khan E, Thomas V, Cheryala V, Shah C, et al. New onset diabetes mellitus in post-COVID-19 patients. J Family Med Prim Care. 2022;11(10):5961. Fadini GP, Morieri ML, Longato E, Avogaro A. Prevalence and impact of diabetes among people infected with SARS-CoV-2. Vol. 43, Journal of Endocrinological Investigation. Springer; 2020. p. 867–9. Sathish T, Kapoor N, Cao Y, Tapp RJ, Zimmet P. Proportion of newly diagnosed diabetes in COVID-19 patients: A systematic review and meta-analysis. Vol. 23, Diabetes, Obesity and Metabolism. Blackwell Publishing Ltd; 2021. p. 870–4. Singh AK, Gupta R, Ghosh A, Misra A. Diabetes in COVID-19: Prevalence, pathophysiology, prognosis and practical considerations. Diabetes and Metabolic Syndrome: Clinical Research and Reviews. 2020 Jul 1;14(4):303–10. Okeahialam B. Article in West African journal of medicine [Internet]. 2011. Available from: https://www.researchgate.net/publication/232745752 John O, Campbell NRC, Brady TM, Farrell M, Varghese C, Velazquez Berumen A, et al. The 2020 ‘WHO Technical Specifications for Automated Non-Invasive Blood Pressure Measuring Devices With Cuff’. Hypertension. 2021 Mar 3;77(3):806–12. Kruijshoop M, Feskens EJM, Blaak EE, De Bruin TWA. Validation of capillary glucose measurements to detect glucose intolerance or type 2 diabetes mellitus in the general population. Clinica Chimica Acta. 2004;341(1–2):33–40. Avan A, Digaleh H, Di Napoli M, Stranges S, Behrouz R, Shojaeianbabaei G, et al. Socioeconomic status and stroke incidence, prevalence, mortality, and worldwide burden: An ecological analysis from the Global Burden of Disease Study 2017. BMC Med. 2019 Oct 24;17(1). Bull FC, Al-Ansari SS, Biddle S, Borodulin K, Buman MP, Cardon G, et al. World Health Organization 2020 guidelines on physical activity and sedentary behaviour. Vol. 54, British Journal of Sports Medicine. BMJ Publishing Group; 2020. p. 1451–62. Li H, Tian S, Chen T, Cui Z, Shi N, Zhong X, et al. Newly diagnosed diabetes is associated with a higher risk of mortality than known diabetes in hospitalized patients with COVID-19. Diabetes Obes Metab. 2020 Oct 1;22(10):1897–906. Lin X, Xu Y, Pan X, Xu J, Ding Y, Sun X, et al. Global, regional, and national burden and trend of diabetes in 195 countries and territories: an analysis from 1990 to 2025. Sci Rep. 2020 Dec 1;10(1). Naveed Z, Velásquez García HA, Wong S, Wilton J, McKee G, Mahmood B, et al. Association of COVID-19 Infection With Incident Diabetes. JAMA Netw Open. 2023 Apr 3;6(4):e238866. Li J, Li Y, Wang Z, Liu N, He L, Zhang H. Increased risk of new-onset diabetes in patients with COVID-19: a systematic review and meta-analysis. Vol. 11, Frontiers in Public Health. Frontiers Media S.A.; 2023. Mathur P, Leburu S, Kulothungan V. Prevalence, Awareness, Treatment and Control of Diabetes in India From the Countrywide National NCD Monitoring Survey. Front Public Health. 2022 Mar 14;10. Rathmann W, Kuss O, Kostev K. Incidence of newly diagnosed diabetes after Covid-19. Available from: https://doi.org/10.1007/s00125-022-05670-0 Taylor K, Eastwood S, Walker V, Cezard G, Knight R, Al Arab M, et al. Diabetes following SARS-CoV-2 infection: Incidence, persistence, and implications of COVID-19 vaccination. A cohort study of fifteen million people. Available from: https://doi.org/10.1101/2023.08.07.23293778 Rooney MR, Fang M, Ogurtsova K, Ozkan B, Echouffo-Tcheugui JB, Boyko EJ, et al. Global Prevalence of Pre-diabetes. Diabetes Care. 2023 Jul 1;46(7):1388–94. Bashir MA, Yahaya AI, Muhammad M, Yusuf AH, Mukhtar IG. Pre-diabetes Burden in Nigeria: A Systematic Review and Meta-Analysis. Vol. 9, Frontiers in Public Health. Frontiers Media S.A.; 2021. Rizvi AA, Kathuria A, Al Mahmeed W, Al-Rasadi K, Al-Alawi K, Banach M, et al. Post-COVID syndrome, inflammation, and diabetes. Vol. 36, Journal of Diabetes and its Complications. Elsevier Inc.; 2022. Khunti K, Prato S Del, Mathieu C, Kahn SE, Gabbay RA, Buse JB. Covid-19, hyperglycemia, and new-onset diabetes. Diabetes Care. 2021 Dec 1;44(12):2645–55. Sharma A, Misra-Hebert AD, Mariam A, Milinovich A, Onuzuruike A, Koomson W, et al. Impacts of COVID-19 on Glycemia and Risk of Diabetic Ketoacidosis. Diabetes. 2023 May 1;72(5):627–37. Wrona M, Skrypnik D. New-Onset Diabetes Mellitus, Hypertension, Dyslipidaemia as Sequelae of COVID-19 Infection—Systematic Review. Vol. 19, International Journal of Environmental Research and Public Health. MDPI; 2022. Rana MA, H. Siddiqui M, Raza S, Tehreem K, Mahmood MFU, Javed M, et al. Incidence of Steroid-induced Diabetes in COVID-19 patients. Pakistan Journal of Medical and Health Sciences. 2021 Oct 30;15(10):2595–6. Additional Declarations Competing interest reported. TTB has served as a consultant to Gilead Sciences, Merck, ViiV Healthcare, and Janssen. Supplementary Files SUPPLEMENTARYMATERIALS.docx Cite Share Download PDF Status: Published Journal Publication published 12 Sep, 2024 Read the published version in BMC Public Health → Version 1 posted Editorial decision: Revision requested 08 Mar, 2024 Submission checks completed at journal 08 Mar, 2024 Editor assigned by journal 08 Mar, 2024 First submitted to journal 22 Feb, 2024 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-3980178","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":277369173,"identity":"12328e5a-5b87-481f-a7b4-c2cfdf17a427","order_by":0,"name":"Roland Stephen","email":"","orcid":"","institution":"Modibbo Adama University Teaching Hospital","correspondingAuthor":false,"prefix":"","firstName":"Roland","middleName":"","lastName":"Stephen","suffix":""},{"id":277369174,"identity":"eecacf89-6666-4fbd-984e-268813055ed2","order_by":1,"name":"Jennifer Tyndal","email":"","orcid":"","institution":"American University of Nigeria","correspondingAuthor":false,"prefix":"","firstName":"Jennifer","middleName":"","lastName":"Tyndal","suffix":""},{"id":277369175,"identity":"03a8c821-2f75-4e18-882f-2183533993f4","order_by":2,"name":"Vivian Hsu","email":"","orcid":"","institution":"Johns Hopkins Bloomberg School of Public Health","correspondingAuthor":false,"prefix":"","firstName":"Vivian","middleName":"","lastName":"Hsu","suffix":""},{"id":277369176,"identity":"13381c26-bdeb-46c5-8cb4-98072e4985eb","order_by":3,"name":"Jing Sun","email":"","orcid":"","institution":"Johns Hopkins Bloomberg School of Public Health","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Sun","suffix":""},{"id":277369177,"identity":"70680871-d27e-4be3-8635-e9da6418a031","order_by":4,"name":"Nura Umaru","email":"","orcid":"","institution":"Federal Teaching Hospital","correspondingAuthor":false,"prefix":"","firstName":"Nura","middleName":"","lastName":"Umaru","suffix":""},{"id":277369178,"identity":"c65e5cef-0a97-4359-ac91-10df268fa4aa","order_by":5,"name":"Jamiu Olumoh","email":"","orcid":"","institution":"American University of Nigeria","correspondingAuthor":false,"prefix":"","firstName":"Jamiu","middleName":"","lastName":"Olumoh","suffix":""},{"id":277369179,"identity":"e822a714-ffd7-4ebb-8274-c887a4f5e755","order_by":6,"name":"Oyelola Adegboye","email":"data:image/png;base64,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","orcid":"","institution":"Charles Darwin University","correspondingAuthor":true,"prefix":"","firstName":"Oyelola","middleName":"","lastName":"Adegboye","suffix":""},{"id":277369180,"identity":"995e0cb7-8c73-4ff4-8c05-64c4310dc7c4","order_by":7,"name":"Olah U. Owobi","email":"","orcid":"","institution":"International Child Health and Social Services Award project (USAID Funded)","correspondingAuthor":false,"prefix":"","firstName":"Olah","middleName":"U.","lastName":"Owobi","suffix":""},{"id":277369181,"identity":"c5fd4a52-309b-4a03-a5d3-9be6ba2e17be","order_by":8,"name":"Todd T. Brown","email":"","orcid":"","institution":"Johns Hopkins University","correspondingAuthor":false,"prefix":"","firstName":"Todd","middleName":"T.","lastName":"Brown","suffix":""}],"badges":[],"createdAt":"2024-02-23 00:29:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3980178/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3980178/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12889-024-19854-3","type":"published","date":"2024-09-12T15:57:27+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":52456014,"identity":"738a6b0b-9a6a-4348-a6da-e554499d7613","added_by":"auto","created_at":"2024-03-11 20:02:32","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":157991,"visible":true,"origin":"","legend":"\u003cp\u003eFlow Chart of Participant Recruitment and Outcomes\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3980178/v1/eae8011c071a6268f8bf1c57.jpg"},{"id":52456013,"identity":"8284b626-c634-4c01-bdfd-69124701b3d3","added_by":"auto","created_at":"2024-03-11 20:02:32","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":4332,"visible":true,"origin":"","legend":"\u003cp\u003ePrevalence of hyperglycaemic between those with a history of COVID-19 COVID (+) and those without COVID (-)\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-3980178/v1/edc629e1af8ac95ca82eb834.png"},{"id":64619128,"identity":"807007bb-b695-49d2-87ab-0073266aca51","added_by":"auto","created_at":"2024-09-16 16:11:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":783887,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3980178/v1/aeaf4a8c-099d-4475-b158-9e009fecfd28.pdf"},{"id":52456012,"identity":"abbb8962-bc19-4510-9db5-02c99b14daed","added_by":"auto","created_at":"2024-03-11 20:02:32","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":21067,"visible":true,"origin":"","legend":"","description":"","filename":"SUPPLEMENTARYMATERIALS.docx","url":"https://assets-eu.researchsquare.com/files/rs-3980178/v1/44715239a6330fefb303d0db.docx"}],"financialInterests":"Competing interest reported. TTB has served as a consultant to Gilead Sciences, Merck, ViiV Healthcare, and Janssen.","formattedTitle":"The prevalence of pre-diabetes and diabetes in people with and without past history of COVID-19 in northeastern Nigeria","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn addition to pneumonia, COVID-19 is associated with multi-systemic complications, such as coagulopathy, myocarditis, and acute kidney injury(\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e). New onset diabetes mellitus (DM) after COVID-19 has also been reported worldwide, predominantly in Asia, Europe and the US (\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e) In a systematic review and metanalysis conducted in 2022, it was reported that males who had contracted COVID-19 had a two-fold risk of developing DM when compared to those without a past history of COVID-19 (\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e). Furthermore, in a related study, the incidence of diabetes post-COVID-19 was estimated to be 15.53 per 1000 person-years (\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e). Moreover, the risk of developing type 2 diabetes following COVID-19 was significantly higher, with a relative risk (RR) of 1.70 (95% confidence interval (CI):1.32\u0026ndash;2.19), when compared to individuals without a past history of COVID-19 (\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e). The etiological mechanisms contributing to post-COVID-19 DM have not been fully elucidated. Potential mechanisms include a direct impact on pancreatic beta cells, which may compromise insulin secretion (\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e). Other factors, such as steroid therapy in the management of COVID-19 infection, have also been implicated in the pathogenesis of post-COVID-19 diabetes (\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eAlthough the impact of COVID-19 pandemic in Africa has not been as pronounced as in other parts of the world, the continent has experienced significant infection rates, resulting in near-term and long-term morbidity and mortality. The data regarding the impact of COVID-19 on DM within the African context is notably limited. The ongoing COVID-19 pandemic may add to the rising burden of DM in Africa, which is already at an epidemic level(\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eThis study retrospectively examines the comparative prevalence of diabetes and pre-diabetes in individuals with and without a past history of COVID-19, thereby investigating the evolving hypothesis that there is an epidemiological link between COVID-19 and abnormal glucose metabolism.\u003c/p\u003e"},{"header":"Methods","content":"\u003ch2\u003eStudy Population\u003c/h2\u003e\n\u003cp\u003ePersons with a history of COVID-19 were identified through (\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e) the hospital admission database of the state\u0026rsquo;s epidemiology unit, Ministry of Health, (\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e) referrals from inpatient care teams for those hospitalized with COVID-19, and 3) identification by the local disease surveillance notification officers (DSNOs) across two States in northeastern Nigeria (Adamawa and Gombe). The DSNOs tracked individuals into their various communities and enlisted them after obtaining informed consent. The diagnosis of COVID-19 was previously established in this cohort using the polymerase chain reaction (PCR) for hospitalized patients and urban dwellers, while the rapid antigen test kits were used for non-hospitalized patients and rural dwellers.\u003c/p\u003e\n\u003cp\u003eWe excluded those below 18 or over 75 years, pregnant women and those who self-reported having diabetes or prediabetes prior to the COVID-19 pandemic.\u003c/p\u003e\n\u003cp\u003eIndividuals without a past history of COVID-19 and did not have a self-reported history of diabetes or prediabetes served as a control group. We recruited participants during a free medical outreach organized in the community, during which free blood sugar and blood pressure were checked. Those who self-reported having diabetes or prediabetes were excluded. Additional control participants were recruited when they presented to the healthcare facility as an outpatient to the general outpatient department or as a patient\u0026rsquo;s relative.\u003c/p\u003e\n\u003cp\u003eEthical approval\u0026nbsp;was obtained from the health research committee of the Modibbo Adama University Teaching Hospital Yola Adamawa State, Nigeria MAUTH, with approval number MAUTHYOLA/HREC/22/237. All participants gave informed consent.\u003c/p\u003e\n\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e\n\u003ch2\u003eData collection\u003c/h2\u003e\n\u003cp\u003eData were collected from December 2022 to April 2023. Trained DSNOs administered the questionnaire by tracking the COVID-19-positive cohort in their communities. A trained nurse enrolled healthcare workers who met the eligibility criteria in their various healthcare facilities. We collected data on sociodemographic information, behavioural risk factors for DM, personal history of hypertension, and family history of hypertension and DM. Glucose was tested using capillary blood (Accucheck) in participants after 8 hours of fast, and their waist-hip ratio was taken. Blood pressure was measured by adhering to the WHO standardized procedure. Blood pressure was assessed with two separate measurements 30 minutes apart after resting for at least 5 minutes in accordance with WHO standardized procedures(\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e). Waist circumference was measured using a flexible tape at the level of the midpoint between the ribs and the iliac crest from the front after exhalation(\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e). Hip circumference was measured using a flexible tape at the point where the buttocks extend the most when viewed from the side(\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eWe targeted a sample size of 256 in each group to detect a difference in diabetes prevalence with an alpha of 0.05 with a power of 80%, assuming a diabetes prevalence in the COVID-19 group of 20.8% (\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003ch2\u003eStudy outcome\u003c/h2\u003e\n\u003cp\u003eThe primary outcome was the prevalence of diabetes and pre-diabetes, defined using criteria established by the American Diabetes Association.(\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e) Diabetes was defined as a fasting glucose concentration\u0026thinsp;\u0026ge;\u0026thinsp;7.0mmol/L and pre-diabetes was defined as a fasting glucose concentration of 5.6\u0026ndash;6.9 mmol/l. Capillary blood was used for glycaemic status assessment(\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e)\u003c/p\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n\u003ch2\u003eCovariates\u003c/h2\u003e\n\u003cp\u003eHypertension was defined as either a systolic blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;140 or diastolic\u0026thinsp;\u0026ge;\u0026thinsp;90 or both or self-reported hypertension on treatment. Central obesity was defined using the adult waist-hip ratio (WHR), male WHR\u0026thinsp;\u0026ge;\u0026thinsp;0.90 and female WHR\u0026thinsp;\u0026ge;\u0026thinsp;0.85(\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e). Physical activity was measured using World Health Organization criteria for physical inactivity(\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e). Physical inactivity was defined as the failure to meet WHO recommendations on physical activity for health, which are defined as engaging in at least 150 minutes of moderate-intensity activity per week or 75 minutes of vigorous-intensity activity per week, through any combination of walking and moderate or vigorous-intensity activities.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n\u003ch2\u003eStatistical analysis\u003c/h2\u003e\n\u003cp\u003eAll statistical analyses were performed using Stata software, version 17.0 and R version 4.0.3. Data were presented as frequencies and percentages for categorical variables. The prevalence of diabetes and pre-diabetes with Clopper-Pearson 95% confidence interval was presented for individuals with and without a history of COVID-19. We assessed the relationship between those with and without a history of COVID-19 and diabetes (normal blood glucose vs. pre-diabetic and diabetic) using multinomial logistic regression. Normal blood glucose was used as the reference. All models were adjusted for age (\u0026lt;\u0026thinsp;50 vs. \u0026gt;=50), gender (male vs. female), hypertension (yes vs. no), family history of DM (yes vs. no), physical activity (active vs. sedentary) and waist-hip-ratio (normal vs. central obesity).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e describes the flowchart for participant recruitment. Initially, 294 potential post-COVID participants were identified; 31 with self-reported diabetes were excluded, as were seven who declined consent, leaving 256 participants that were included in this study. Of these, 44 were hospitalized, with 16 testing their baseline blood sugar levels at diagnosis; 4 of these developed new-onset diabetes, while the rest were euglycemic. The remaining 240 did not have baseline blood sugar checked. For the control cohort, 300 potential participants were identified, but 44 with self-reported diabetes were excluded.\u003c/p\u003e\n\u003cp\u003eParticipant characteristics for the two groups are given in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. Those with a history of COVID-19 were more likely to be male than those without a history of COVID-19 (62.5% vs 49.4%. \u003cem\u003ep\u0026thinsp;=\u0026thinsp;0.002\u003c/em\u003e). Significant differences in marital status between the two groups were observed. For instance, there were nearly twice as many single individuals in the COVID-19 cohort compared to the control group (26.5% vs. 14.5%). Also, while the majority were married in both cohorts, there were slightly more so in the control group (69.9% vs. 76.2%). Those with a history COVID-19 were more likely to have a tertiary education, and less like to have no formal education compared to the control group. The occupations varied significantly between the two groups (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with the COVID-19 cohort having more civil servants (27.3% vs. 8.2%) and healthcare workers (16% vs. 0%).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eBaseline demographic characteristics of study participants\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCharacteristics\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePositive Past medical history of COVID-19 cohort\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eControl cohort\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ep-value\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eAge, years\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e39.84\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e40.74\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.25\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e160 (62.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e126 (49.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFemale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e96 (37.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e130 (50.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eMarital Status\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDivorced\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3 (1.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9 (3.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMarried\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e179 (69.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e195 (76.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSingle\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e68 (26.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e37 (14.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWidowed\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6 (2.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15 (5.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eEducation\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo formal education\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e30 (11.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e149 (58.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePrimary school\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e29 (11.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e19 (7.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSecondary\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e96 (37.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e54 (21.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTertiary\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e101 (39.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e34 (13.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eOccupation\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eArtisan\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3 (1.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14 (5.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCivil Servant\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e70 (27.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e21 (8.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFarmer\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e61 (23.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e93 (36%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFull time housewife\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e21 (8.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e36 (14.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHealthcare Worker\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e41 (16%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0 (0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNone\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 (0.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14 (5.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eStudent\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e30 (11.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5 (2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTrader/Business\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25 (9.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e57 (22.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOthers\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3 (1.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16 (6.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eBlood pressure\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.059\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHypertensive\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e68 (26.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e50 (19.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNormotensive\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e188 (73.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e206 (80.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eFam Hx of NCD\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDiabetes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5 (2.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7 (3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDiabetes/ Hypertension\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15 (5.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2 (1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHypertension\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e103 (40.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e27 (11%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUknown\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e27 (10.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18 (7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNone\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e106 (41.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e202 (79%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePhysical Activity\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.658\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eActive\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e135 (53%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e130 (50.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSedentary\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e121 (47%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e126 (49.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eAdmission (weeks)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHospitalized\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e44 (17%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNot-Hospitalized\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e212 (83%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eNew onset in-hospital DM\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e252 (98%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4 (2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eSteroid use\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e240 (94%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16 (6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eWaist-Hip-ratio\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.063\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ecentral obesity\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e123 (48%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e144 (56.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003enormal build\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e133 (52%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e112 (43.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eGlycaemic status\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.268\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDiabetic\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12 (4.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4 (1.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePrediabetes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e68 (26.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11 (4.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEuglycemic\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e176 (68.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e241 (94.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe prevalence of diabetes in the COVID-19 and the control groups were 4.6% (0.046, 95% CI: 0.024, 0.08)) and 1.6% (0.016, 95% CI: 0.004, 0.039), respectively (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). The prevalence of pre-diabetes in the COVID-19 and the control cohorts were 26.6% and 4.3%, respectively. Results from univariate and multivariable logistic regression models for the associated risk factors of hyperglycaemia (diabetes or pre-diabetes) (Supplementary Table S1A-B) show a substantially increased likelihood of hyperglycaemia (fasting glucose \u0026ge; 100 mg/dL) among individuals with a past COVID-19 infection compared to those without such a history, in both univariate and multivariate models (OR\u0026thinsp;=\u0026thinsp;7.30, 95% CI: 4.07, 13.11; aOR\u0026thinsp;=\u0026thinsp;6.58, 95% CI: 3.54, 12.21). Furthermore, while hypertension (OR\u0026thinsp;=\u0026thinsp;1.95, 95% CI: 1.20, 3.18), and physical inactivity (OR\u0026thinsp;=\u0026thinsp;2.36, 95% CI: 1.48, 3.75) were significant risk factors for hyperglycemia in univariate models, only physical inactivity (aOR\u0026thinsp;=\u0026thinsp;2.58, 95% CI: 1.51, 4.41) was a significant risk factors for hyperglycemia after adjusting for other variables in the model. Age, gender, family history of diabetes, and central obesity was not associated with prevalent diabetes in this cohort.\u003c/p\u003e\n\u003cp\u003eIn the multivariable multinomial logistic regression models (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e), the risk of pre-diabetes (vs normoglycemia) was about eight-fold higher in those with a history of COVID-19 (RRR: 7.55, 95% CI: 3.76\u0026ndash;15.17, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) compared to those without a history of COVID-19. Similarly, the risk of diabetes (vs normoglycemia) was three-fold higher in those with a history of COVID-19 3.97 (RRR: 3.44, 95% CI: 1.01\u0026ndash;11.71, p\u0026thinsp;=\u0026thinsp;0.028) compared to those without a history of COVID-19. Age and gender were not a significant predictor of having pre-diabetes or diabetes. Hypertension was also not significantly associated with having pre-diabetes but was associated with having diabetes (RRR\u0026thinsp;=\u0026thinsp;4.63, 95% CI: 1.45, 14.84, p\u0026thinsp;=\u0026thinsp;0.01). Individuals with low physical activity had higher odds of having pre-diabetes (RRR\u0026thinsp;=\u0026thinsp;2.87, 95% CI: 1.57, 5.24, p\u0026thinsp;=\u0026thinsp;0.001) and diabetes (RRR\u0026thinsp;=\u0026thinsp;4.86, 95% CI: 1.43, 16.56, p\u0026thinsp;=\u0026thinsp;0.011) compared to active individuals. Results from sensitivity analyses excluding the 16 participants who received steroids during admission for COVID-19 were similar (Supplementary Table S2).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eMultinomial logistic regression of the risk of diabetes and pre-diabetes in individuals with and without a past history of COVID-19 (exclusive of those treated with steroids).\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003ePre-diabetic vs. Normal\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eDiabetic vs. Normal\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eVariable\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRRR (95% CI)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ep-value\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRRR (95% CI)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ep-value\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCOVID-19\u003c/p\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRef.\u003c/p\u003e\n\u003cp\u003e7.55 (3.76, 15.17)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRef.\u003c/p\u003e\n\u003cp\u003e3.44 (1.01, 11.71)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e0.048\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAge\u003c/p\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;50\u003c/p\u003e\n\u003cp\u003e\u0026gt;=50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRef.\u003c/p\u003e\n\u003cp\u003e0.68 (0.34, 1.38)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e0.291\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRef.\u003c/p\u003e\n\u003cp\u003e2.04 (0.66, 6.31)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e0.216\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGender\u003c/p\u003e\n\u003cp\u003eMale\u003c/p\u003e\n\u003cp\u003eFemale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRef.\u003c/p\u003e\n\u003cp\u003e0.77 (0.43,1.38)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e0.377\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRef.\u003c/p\u003e\n\u003cp\u003e0.54 (0.15, 1.86)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e0.326\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHypertension\u003c/p\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRef.\u003c/p\u003e\n\u003cp\u003e1.45 (0.76, 2.78)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e0.261\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRef.\u003c/p\u003e\n\u003cp\u003e4.23 (1.35, 13.28)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e0.014\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFamily history of DM\u003c/p\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRef.\u003c/p\u003e\n\u003cp\u003e1.47 (0.63, 3.40)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e0.372\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRef.\u003c/p\u003e\n\u003cp\u003e0.64 (0.07, 5.54)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e0.686\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePhysical activity\u003c/p\u003e\n\u003cp\u003eActive\u003c/p\u003e\n\u003cp\u003eSedentary\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRef.\u003c/p\u003e\n\u003cp\u003e2.33 (1.31, 4.13)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e0.004\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRef.\u003c/p\u003e\n\u003cp\u003e4.18 (1.24, 14.04)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e0.021\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWaist-hip-ratio\u003c/p\u003e\n\u003cp\u003eNormal\u003c/p\u003e\n\u003cp\u003eCentral obesity\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRef.\u003c/p\u003e\n\u003cp\u003e1.72 (0.93, 3.18)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e0.082\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRef.\u003c/p\u003e\n\u003cp\u003e1.04 (0.33, 3.25)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e0.952\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eTo the best of our knowledge, this is the first study to examine DM and pre-diabetes prevalence in those with and without a history of COVID-19 in Africa, an area that has one of the fastest-growing populations with DM. We showed that the prevalence of DM and pre-DM in persons with a known history of COVID-19 was higher than in those without a history of COVID-19. Our findings suggest that COVID-19 may be a risk factor for pre-diabetes and diabetes in this population.\u003c/p\u003e \u003cp\u003eIn our study based in Northeastern Nigeria, we found the prevalence of diabetes mellitus to be higher among those with a history of COVID-19 compared to participants without a known history of COVID-19. The observed prevalence in those with a history of COVID-19 was higher than the subregional prevalence of DM of 3.8% (95% CI: 2.7\u0026ndash;4.7)(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Previous studies have also shown a higher risk of diabetes related to COVID-19. In a recent Canadian study, the risk of diabetes in individuals with a history of COVID-19 was 0.5%, as opposed to 0.4% in those without COVID-19 (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). The study further revealed that COVID-19 accounted for 3.41% of DM in the studied population. In their systematic review and metanalysis, Jiajun \u003cem\u003eet a\u003c/em\u003el (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e), revealed that a history of COVID-19 increased the risk of DM and hyperglycaemia by 1.7 fold compared to those without a history of COVID-19. In the Indian experience, new-onset diabetes among individuals with a history of COVID-19 was observed in 16.7% three months post COVID-19 (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Though this study implicated other factors such as age, adiposity, and family history of diabetes in post COVID-19 DM, the observed prevalence is higher than the national prevalence of DM in India of 9.3%(\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). At variance with our study, a Chinese-based study reported a 10.3% prevalence of diabetes among COVID-19 patients, which showed almost no difference in the prevalence of 10.9% in the general Chinese population (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Additionally, Rathmann et al(\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e) reported a higher incidence of diabetes in those with a history of COVID-19 than in those with other types of common acute viral upper respiratory infections (15.8 vs. 12.3 per 1000 person-years) in Germany(\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe high prevalence of mild COVID-19 could possibly account for the relatively lower prevalence of diabetes in our study. Studies have shown that the incidence risk of post COVID-19 DM increases with the severity of COVID-19 (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). According to a systematic review, the global prevalence of pre-diabetes is 5.8% (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e), while the pooled national prevalence of pre-diabetes in Nigeria was estimated at 13.2% (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Given that pre-diabetes is a major risk factor for the development of DM, identification of this population may have important public health consequences.\u003c/p\u003e \u003cp\u003eIt is thought that COVID-19 triggers the pathogenic process of DM by first initiating insulin resistance, then pre-diabetes and finally frank diabetes(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). In addition, other pathophysiologic mechanisms are heightened inflammatory state, disruption of the angiotensin-converting enzyme ACEI/ACE2 balance and subsequent dysfunction of the renin-angiotensin aldosterone-system RAAS (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Of note is the direct pancreatic beta cell destruction by the SARS-COV2 (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). The extent to which this population is at risk of the development of diabetes mellitus deserves further study.\u003c/p\u003e \u003cp\u003eThe relationship between COVID-19 and diabetes mellitus may be bidirectional (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). Hence, it is possible that the cases of previously undiagnosed diabetes are being recognized during a clinical encounter related to COVID-19. This possibility was suggested by a US-based study which showed that persons with COVID-19 were 40% more likely to be diagnosed with diabetes mellitus compared to those without COVID-19 but were 274% more likely to get diagnosed with DM with the same pre-COVID HbA1c (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). In our study, although participants with a history of COVID-19 reported no pre-existing diabetes prior to study assessment, it is possible that hyperglycaemia was present at the time of COVID-19 diagnosis but was not clinically apparent.\u003c/p\u003e \u003cp\u003eFor severe COVID-19, glucocorticoids are indicated for lung disease, which may, in turn, increase the risk of diabetes mellitus(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). For example, in an Indian study, of the 31 individuals who had been treated with steroids while on admission for COVID-19, eleven (35.5%) went on to develop new-onset diabetes three months later (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Our study results are unlikely to be related to glucocorticoid use. In a sensitivity analysis, excluding those who reported glucocorticoid use as a treatment for COVID-19, the higher odds of prevalent diabetes and pre-diabetes among those with COVID-19 compared to the control group persisted.\u003c/p\u003e \u003cp\u003eWe acknowledge the following limitations in our study. First, most of our participants did not have a glucose determination at the time of or before their COVID-19 diagnosis. We, therefore, are unable to fully exclude persons with pre-existing diabetes. Second, we used capillary glucose for diabetes determination, which, while suitable for epidemiologic studies, may be slightly different than plasma glucose. An additional assessment with HbA1c could also have been useful to better categorize glycaemic status. Similarly, we did not carry out COVID-19 antibody testing for the control group. Being predominantly retrospective in design, it is not shielded from the inherent recall bias of this design. Lastly, the two cohorts differed on important characteristics, which we attempted to balance with multivariable adjustment.\u003c/p\u003e \u003cp\u003eIn conclusion, our study suggests that a history of COVID-19 may be a risk factor for diabetes and pre-diabetes in Nigeria. If confirmed in longitudinal studies, more aggressive screening for DM may be warranted among those who have a history of COVID-19. Further studies are also needed to determine the risk of transition to DM in those with a history of COVID-19 with pre-diabetes and what interventions can be implemented to decrease this risk.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRIS and JT conceived the project and drafted the manuscript. VH, JS and OA conducted data analysis JT, OA and TTB supervised the methodological approach and interpretation of results. RIS, JT, VH, JS, NU, JO, OA, OUO and TTB contributed to study design, introduction, discussion and critical interpretation of results. JT and RIS led ethics application and project management. All authors critically revised the manuscript for intellectual content. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTTB is supported in part by K24 AI120834\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclosures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTTB has served as a consultant to Gilead Sciences, Merck, ViiV Healthcare, and Janssen.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe dataset supporting the conclusions of this article is included within the article (and its additional files)\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWhite-Dzuro G, Gibson LE, Zazzeron L, White-Dzuro C, Sullivan Z, Diiorio DA, et al. Multisystem effects of COVID-19: a concise review for practitioners. Vol. 133, Postgraduate Medicine. Bellwether Publishing, Ltd.; 2021. p. 20\u0026ndash;7. \u003c/li\u003e\n\u003cli\u003eZhang T, Mei Q, Zhang Z, Walline JH, Liu Y, Zhu H, et al. Risk for newly diagnosed diabetes after COVID-19: a systematic review and meta-analysis. BMC Med. 2022 Dec 1;20(1):444. \u003c/li\u003e\n\u003cli\u003eShrestha DB, Budhathoki P, Raut S, Adhikari S, Ghimire P, Thapaliya S, et al. New-onset diabetes in COVID-19 and clinical outcomes: A systematic review and meta-analysis. World J Virol. 2021 Sep 25;10(5):275\u0026ndash;87. \u003c/li\u003e\n\u003cli\u003eZhang T, Mei Q, Zhang Z, Walline JH, Liu Y, Zhu H, et al. Risk for newly diagnosed diabetes after COVID-19: a systematic review and meta-analysis. BMC Med. 2022 Dec 1;20(1):444. \u003c/li\u003e\n\u003cli\u003eLim S, Bae JH, Kwon HS, Nauck MA. COVID-19 and diabetes mellitus: from pathophysiology to clinical management. Vol. 17, Nature Reviews Endocrinology. Nature Research; 2021. p. 11\u0026ndash;30. \u003c/li\u003e\n\u003cli\u003eDallavalasa S, Tulimilli SR V., Prakash J, Ramachandra R, Madhunapantula SR V., Veeranna RP. COVID-19: Diabetes Perspective\u0026mdash;Pathophysiology and Management. Vol. 12, Pathogens. MDPI; 2023. \u003c/li\u003e\n\u003cli\u003eZhang T, Mei Q, Zhang Z, Walline JH, Liu Y, Zhu H, et al. Risk for newly diagnosed diabetes after COVID-19: a systematic review and meta-analysis. BMC Med. 2022 Dec 1;20(1). \u003c/li\u003e\n\u003cli\u003eLim S, Bae JH, Kwon HS, Nauck MA. COVID-19 and diabetes mellitus: from pathophysiology to clinical management. Vol. 17, Nature Reviews Endocrinology. Nature Research; 2021. p. 11\u0026ndash;30. \u003c/li\u003e\n\u003cli\u003eDessie G, Mulugeta H, Amare D, Negesse A, Wagnew F, Getaneh T, et al. A systematic analysis on prevalence and sub-regional distribution of undiagnosed diabetes mellitus among adults in African countries. Vol. 19, Journal of Diabetes and Metabolic Disorders. Springer Science and Business Media Deutschland GmbH; 2020. p. 1931\u0026ndash;41. \u003c/li\u003e\n\u003cli\u003eLi H, Tian S, Chen T, Cui Z, Shi N, Zhong X, et al. Newly diagnosed diabetes is associated with a higher risk of mortality than known diabetes in hospitalized patients with COVID-19. Diabetes Obes Metab. 2020 Oct 1;22(10):1897\u0026ndash;906. \u003c/li\u003e\n\u003cli\u003eUloko Baba M Musa Mansur A Ramalan Ibrahim D Gezawa Fabian H Puepet Ayekame T Uloko Musa M Borodo Kabiru B Sada AE. Prevalence and Risk Factors for Diabetes Mellitus in Nigeria: A Systematic Review and Meta-Analysis. Diabetes Therapy [Internet]. 9. Available from: https://doi.org/10.6084/\u003c/li\u003e\n\u003cli\u003eKeerthi B, Sushmita G, Khan E, Thomas V, Cheryala V, Shah C, et al. New onset diabetes mellitus in post-COVID-19 patients. J Family Med Prim Care. 2022;11(10):5961. \u003c/li\u003e\n\u003cli\u003eFadini GP, Morieri ML, Longato E, Avogaro A. Prevalence and impact of diabetes among people infected with SARS-CoV-2. Vol. 43, Journal of Endocrinological Investigation. Springer; 2020. p. 867\u0026ndash;9. \u003c/li\u003e\n\u003cli\u003eSathish T, Kapoor N, Cao Y, Tapp RJ, Zimmet P. Proportion of newly diagnosed diabetes in COVID-19 patients: A systematic review and meta-analysis. Vol. 23, Diabetes, Obesity and Metabolism. Blackwell Publishing Ltd; 2021. p. 870\u0026ndash;4. \u003c/li\u003e\n\u003cli\u003eSingh AK, Gupta R, Ghosh A, Misra A. Diabetes in COVID-19: Prevalence, pathophysiology, prognosis and practical considerations. Diabetes and Metabolic Syndrome: Clinical Research and Reviews. 2020 Jul 1;14(4):303\u0026ndash;10. \u003c/li\u003e\n\u003cli\u003eOkeahialam B. Article in West African journal of medicine [Internet]. 2011. Available from: https://www.researchgate.net/publication/232745752\u003c/li\u003e\n\u003cli\u003eJohn O, Campbell NRC, Brady TM, Farrell M, Varghese C, Velazquez Berumen A, et al. The 2020 \u0026lsquo;WHO Technical Specifications for Automated Non-Invasive Blood Pressure Measuring Devices With Cuff\u0026rsquo;. Hypertension. 2021 Mar 3;77(3):806\u0026ndash;12. \u003c/li\u003e\n\u003cli\u003eKruijshoop M, Feskens EJM, Blaak EE, De Bruin TWA. Validation of capillary glucose measurements to detect glucose intolerance or type 2 diabetes mellitus in the general population. Clinica Chimica Acta. 2004;341(1\u0026ndash;2):33\u0026ndash;40. \u003c/li\u003e\n\u003cli\u003eAvan A, Digaleh H, Di Napoli M, Stranges S, Behrouz R, Shojaeianbabaei G, et al. Socioeconomic status and stroke incidence, prevalence, mortality, and worldwide burden: An ecological analysis from the Global Burden of Disease Study 2017. BMC Med. 2019 Oct 24;17(1). \u003c/li\u003e\n\u003cli\u003eBull FC, Al-Ansari SS, Biddle S, Borodulin K, Buman MP, Cardon G, et al. World Health Organization 2020 guidelines on physical activity and sedentary behaviour. Vol. 54, British Journal of Sports Medicine. BMJ Publishing Group; 2020. p. 1451\u0026ndash;62. \u003c/li\u003e\n\u003cli\u003eLi H, Tian S, Chen T, Cui Z, Shi N, Zhong X, et al. Newly diagnosed diabetes is associated with a higher risk of mortality than known diabetes in hospitalized patients with COVID-19. Diabetes Obes Metab. 2020 Oct 1;22(10):1897\u0026ndash;906. \u003c/li\u003e\n\u003cli\u003eLin X, Xu Y, Pan X, Xu J, Ding Y, Sun X, et al. Global, regional, and national burden and trend of diabetes in 195 countries and territories: an analysis from 1990 to 2025. Sci Rep. 2020 Dec 1;10(1). \u003c/li\u003e\n\u003cli\u003eNaveed Z, Vel\u0026aacute;squez Garc\u0026iacute;a HA, Wong S, Wilton J, McKee G, Mahmood B, et al. Association of COVID-19 Infection With Incident Diabetes. JAMA Netw Open. 2023 Apr 3;6(4):e238866. \u003c/li\u003e\n\u003cli\u003eLi J, Li Y, Wang Z, Liu N, He L, Zhang H. Increased risk of new-onset diabetes in patients with COVID-19: a systematic review and meta-analysis. Vol. 11, Frontiers in Public Health. Frontiers Media S.A.; 2023. \u003c/li\u003e\n\u003cli\u003eMathur P, Leburu S, Kulothungan V. Prevalence, Awareness, Treatment and Control of Diabetes in India From the Countrywide National NCD Monitoring Survey. Front Public Health. 2022 Mar 14;10. \u003c/li\u003e\n\u003cli\u003eRathmann W, Kuss O, Kostev K. Incidence of newly diagnosed diabetes after Covid-19. Available from: https://doi.org/10.1007/s00125-022-05670-0\u003c/li\u003e\n\u003cli\u003eTaylor K, Eastwood S, Walker V, Cezard G, Knight R, Al Arab M, et al. Diabetes following SARS-CoV-2 infection: Incidence, persistence, and implications of COVID-19 vaccination. A cohort study of fifteen million people. Available from: https://doi.org/10.1101/2023.08.07.23293778\u003c/li\u003e\n\u003cli\u003eRooney MR, Fang M, Ogurtsova K, Ozkan B, Echouffo-Tcheugui JB, Boyko EJ, et al. Global Prevalence of Pre-diabetes. Diabetes Care. 2023 Jul 1;46(7):1388\u0026ndash;94. \u003c/li\u003e\n\u003cli\u003eBashir MA, Yahaya AI, Muhammad M, Yusuf AH, Mukhtar IG. Pre-diabetes Burden in Nigeria: A Systematic Review and Meta-Analysis. Vol. 9, Frontiers in Public Health. Frontiers Media S.A.; 2021. \u003c/li\u003e\n\u003cli\u003eRizvi AA, Kathuria A, Al Mahmeed W, Al-Rasadi K, Al-Alawi K, Banach M, et al. Post-COVID syndrome, inflammation, and diabetes. Vol. 36, Journal of Diabetes and its Complications. Elsevier Inc.; 2022. \u003c/li\u003e\n\u003cli\u003eKhunti K, Prato S Del, Mathieu C, Kahn SE, Gabbay RA, Buse JB. Covid-19, hyperglycemia, and new-onset diabetes. Diabetes Care. 2021 Dec 1;44(12):2645\u0026ndash;55.\u003c/li\u003e\n\u003cli\u003eSharma A, Misra-Hebert AD, Mariam A, Milinovich A, Onuzuruike A, Koomson W, et al. Impacts of COVID-19 on Glycemia and Risk of Diabetic Ketoacidosis. Diabetes. 2023 May 1;72(5):627\u0026ndash;37. \u003c/li\u003e\n\u003cli\u003eWrona M, Skrypnik D. New-Onset Diabetes Mellitus, Hypertension, Dyslipidaemia as Sequelae of COVID-19 Infection\u0026mdash;Systematic Review. Vol. 19, International Journal of Environmental Research and Public Health. MDPI; 2022. \u003c/li\u003e\n\u003cli\u003eRana MA, H. Siddiqui M, Raza S, Tehreem K, Mahmood MFU, Javed M, et al. Incidence of Steroid-induced Diabetes in COVID-19 patients. Pakistan Journal of Medical and Health Sciences. 2021 Oct 30;15(10):2595\u0026ndash;6. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"COVID-19, Diabetes Mellitus, Nigeria, Glycaemic Assessment, Risk Factors","lastPublishedDoi":"10.21203/rs.3.rs-3980178/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3980178/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAn increased risk of diabetes mellitus (DM) after COVID-19 has been reported in the United States, Europe, and Asia. The burden of COVID-related DM has not been described in Africa, where the overall risk of DM has been increasing rapidly. Our objective was to compare the prevalence of pre-DM and DM in Nigerian individuals with a history of COVID-19 to individuals without known COVID-19 infection.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe identified 256 individuals with a past medical history of COVID-19 with no history of pre-DM or DM and 256 individuals without a history of COVID-19 or pre-DM/DM. Participants were categorized as pre-DM (fasting capillary glucose 100\u0026ndash;125 mg/dL) or DM (fasting capillary glucose\u0026thinsp;\u0026ge;\u0026thinsp;126 mg/dL). We used multivariate multinomial logistic regression to determine the odds of pre-DM and DM in those with and without a history of COVID-19 after adjustment for age, gender, the presence of hypertension, physical activity, central adiposity, and family history of DM.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eCompared to the control group, those with a history of COVID-19 had a similar median age (38 vs 40 years, p\u0026thinsp;=\u0026thinsp;0.84), had a higher proportion of men (63% vs 49%), and had a lower prevalence of central adiposity (waist: hip ratio\u0026thinsp;\u0026ge;\u0026thinsp;0.90 for males and WHR\u0026thinsp;\u0026ge;\u0026thinsp;0.85 for females) (48% vs 56.3%, p\u0026thinsp;=\u0026thinsp;0.06). Of the 256 with a history of COVID-19, 44 (17%) required inpatient care. The median (interquartile range) time interval between COVID-19 diagnosis and the glycaemic assessment was 19 (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e) months. Pre-DM prevalence was 27% in the post-COVID-19 group and 4% in the control group, whereas the prevalence of DM was 7% in the post-COVID-19 group and 2% in the control group. After multivariable adjustment, the odds of pre-DM were 8.12 (95% confidence interval (CI): 33.98, 16.58; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) higher, and the odds of DM were 3.97 (95% CI: 1.16, 13.63) higher in those with a history of COVID-19 compared to controls.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003ePrevious COVID-19 was found to be a risk factor for prevalent pre-diabetes and diabetes mellitus in Nigeria. More intensive screening for DM in those with a history of COVID-19 should be considered.\u003c/p\u003e","manuscriptTitle":"The prevalence of pre-diabetes and diabetes in people with and without past history of COVID-19 in northeastern Nigeria","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-11 20:02:27","doi":"10.21203/rs.3.rs-3980178/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-03-08T11:29:23+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-03-08T07:15:04+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-03-08T07:15:04+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2024-02-23T00:19:57+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"967b6550-1e0a-4941-b1c3-e1522b812824","owner":[],"postedDate":"March 11th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-09-16T16:01:37+00:00","versionOfRecord":{"articleIdentity":"rs-3980178","link":"https://doi.org/10.1186/s12889-024-19854-3","journal":{"identity":"bmc-public-health","isVorOnly":false,"title":"BMC Public Health"},"publishedOn":"2024-09-12 15:57:27","publishedOnDateReadable":"September 12th, 2024"},"versionCreatedAt":"2024-03-11 20:02:27","video":"","vorDoi":"10.1186/s12889-024-19854-3","vorDoiUrl":"https://doi.org/10.1186/s12889-024-19854-3","workflowStages":[]},"version":"v1","identity":"rs-3980178","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3980178","identity":"rs-3980178","version":["v1"]},"buildId":"J0_U0BvcaRcwD8yVFaRlm","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}