Diabetes was more in those aged 60 years and above in this study. This could likely be attributed to the fact that diabetes increases with age due to insulin resistance. On the other hand, Oyegbade OO and colleagues in a community study in Ile Ife, Osun State65, observed that diabetes was commoner in those aged 45 years and above and that sex had significant association with type 2 diabetes. The difference in age at which diabetes prevalence was more could be due
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to the difference in the age group cut-off point in both studies. While this study was a hospital population study, that of Ile Ife was an urban community population study.
The age range of subjects in this study was 18 years to 77 years which was similar to that of tertiary hospital in Owerri of 19 years to 76 years. The male/female ratio of the tertiary hospital study in Owerri of 1:5.239 was similar to that of this study of 1:6.5. There was similarity in the age range and male to female ratio of both study population.
In this study, age had an independent association with type 2 diabetes in the respondents, in logistic regression analysis of the risk factors of diabetes. In Turkey, in a community population study, an independent association was reported between type 2 diabetes and age32. Hussain A. and colleagues, in a community population study in Bangladesh, reported significant association between age, body mass index and diabetes30. In Uyo, Nigeria, Ekpenyong CE and colleagues in a community population study, observed that age was significantly associated with non-communicable diseases such as type 2 diabetes26. The similarity with these studies could be due to the fact that insulin resistance increases with age and hence, diabetes is more likely to occur with aging.
In this study, type 2 diabetes was commoner in male respondents. There was no significant association between sex and type 2 diabetes in the respondents. Similarly, Valdez and colleagues in USA in a community population study did not find significant association between sex and type 2 diabetes64. In a meta-analysis study by Cowie CC and colleagues in USA31in a
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community population study, the prevalence of diabetes was reported to be significantly higher in males in the general population similar to the findings of this study though not statistically significant. In the non-Hispanic blacks, prevalence was higher in females, unlike the findings in this study. Hussain A and colleagues in Bangladesh in a community population study, observed higher prevalence of type 2 diabetes in females though not statistically significant14, and when other risk factors were adjusted for, sex showed statistically significant association with diabetes in females, unlike in this study, where males had higher prevalence of diabetes and after adjusting for other risk factors, sex showed no statistically significant association with diabetes in the respondents. The similarity in the sex not been significant could be due to the low number of male subjects in this study compared to that of the Bangladeshi study. Ekpenyong in Uyo, Nigeria, reported slightly higher prevalence of diabetes in males26. This is similar to the findings of this study. Possible reason could be similarity of race and region of the country of the subjects.
In this study, diabetes was commoner in subjects with family history of diabetes in the respondents. There was an independent significant association between family history and diabetes in the respondents. The odds of having diabetes in those with family history of diabetes was high in the respondents (OR = 3.718)[CI = 1.438-9.613]. Similarly, Mumu SJ and colleagues in a community population study, found independent association between family history of diabetes and diabetes in a Bangladesh population63. This was equally similar to that
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observed by Oyegbade OO and colleagues in a community population study in Ile Ife, Osun State, Nigeria65. The similarity between these studies and this study could likely be due to similarity in race. Also, Motala AA and colleagues in a community population study in South Africa; and Aksu H and colleagues in a community population study in Turkey had similar observation in their studies32,60. Sukurai M and colleagues in a community population cohort study in Japan observed that those with family history of diabetes had 80% greater risk of incident diabetes compared with those without family history of diabetes72. In this study, diabetes was commonest among respondents with first degree family relatives that had diabetes 30.8%(4). Most community population based studies found significant association between family history of diabetes and diabetes bringing to the fore the strong part played by gene in having diabetes. In USA in a community population study, when family history was graded into high risk (subjects with two first degree relatives with diabetes); moderate risk (subjects with one first degree relatives and one second degree relatives) and average risk (subjects with no family history of diabetes), graded risk of type 2 diabetes was observed in decreasing order from high risk to average risk64. This was similar to the finding in this study. First degree relative family history of diabetes had highest prevalence 30.8%(4), followed by those with second degree family relatives 718.9%(7). This study did not grade the risk of diabetes in similar fashion as that of the American study. This brings to the fore the need to screen adult hospital clients with family history of diabetes.
82 5.4 MODIFIABLE RISK FACTORS
In this study, locality had no association with diabetes in the respondents. Rural respondents had less odds for diabetes (OR = 0.557)[CI = 0.210-1.475], though it is not statistically significant. About 55.0% of the non-obese lived in the rural areas and they may likely be doing manual jobs and eating natural food, unlike 72.1% of obese that lived in the urban area with its attendant western lifestyle32. There was no independent statistically significant association between locality and diabetes in this study. Balde and colleagues in a community population study in Guinea, similarly, observed significant association between diabetes and urban location37. The similarity between the finding in this study and that of the above study could be largely attributed to similarity in race and socio-demographic factors. They all belong to low- and middle-income countries. Also, Motola AA and colleagues in South Africa, found significant association between the history of urban living and diabetes in males60, though this effect was lost after adjusting for other risk factors.
In this study, diabetes was more in those with low literacy level (primary school education) 12(15.4%) then followed by those with secondary education 4(6.3%) in the respondents. There was no association between educational status and diabetes. Mumu SJ and colleagues in a community population cross-sectional analytical study in Bangladesh observed that high literacy level was associated with good knowledge and attitude of risk factors of diabetes63. Another
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study in Bangladesh observed that diabetes was more prevalent in those with high literacy level and that high literacy level had independent association with diabetes14. This study on the contrary found that diabetes was more in those with low literacy level. High socioeconomic status and high literacy have been associated with increased risk of diabetes in developing countries27. This could be partly attributed to belief that when one eats and puts on weight that it is an evidence of wealth and good living unlike in developed countries, where high socioeconomic status or high literacy result in more discretion in feeding habit and lifestyle32. Again difference in locality of both populations could be a contributing factor. A rural population study in Imo State observed that low literacy level was more in the obese54 just as was observed in the Inter Act Consortium study72.
Diabetes was more prevalent among traders and civil servants and there was no significant association between occupational status and diabetes in the responders. In this environment, most traders sit down waiting for customers (sedentary lifestyle) most of the time, eat a lot of junk food like snacks, soft drinks and when they want to eat food may likely patronize the food vendors. The food vendors often spice their food with monosodium glutamate and other spices to make it tasty. On the other hand, Azimi-Nezhad M and colleagues in a community population study in Iran did observe significant association between occupation and diabetes, which was highest among the retirees, followed by the unemployed and the
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housewives81. This was similar to the finding by Peykari N and colleagues in a community population study in Iran too82.
Diabetes was more prevalent in the widowed and the married in the responders. Marital status had no significant association with diabetes in the responders. Similarly, Hwang J and colleagues in a community population study in Korea observed higher prevalence of diabetes among the married and the partners than in the singles83.The similarity could be due to the high proportion of the married in both populations. Azimi-Nezhad M and colleagues in a community population study in Iran observed significant association in the prevalence of diabetes among married, singles, widowed and divorced subjects81, unlike this study which did not show any statistically significant association between marital status and diabetes.
In this study, there was significant weak positive correlation between random blood sugar and waist circumference in the respondents. Waist circumference is expected to correlate positively with blood glucose. There was no significant association between BMI and type 2 diabetes in this study. Surprisingly, Frank LK and colleagues in a case–control tertiary hospital population study in Ghana observed that neither BMI nor WC had any association with type 2 diabetes, even when those with poor glucose control among the cases were excluded 84. In this study however, those with poor blood glucose control were not excluded. Park SW and colleagues had suggested that in diabetics, poor glucose control was the underlying mechanism
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for reduced BMI and mean body cell mass 85. Many studies within and outside Africa had found significant association between high body mass index, increased waist circumference and type 2 diabetes 23,31,32,33 . In different regions of Africa, studies with different study designs, that used different obesity measurements, different diabetes measurements, and different sample sizes had observed different types of association between BMI and diabetes. In some, BMI was consistently associated with diabetes both in males and females. In others, association differed in males and females, while in other there was no association 14, 37, 59, 60. Extraneous confounders may be possible explanation for the masking of the association between BMI and diabetes in this study. On the other hand, it seems illogical for it to explain why family history of diabetes, age, waist circumference, average systolic pressure had significant association with diabetes. The likely explanation could be racial inappropriate cut-off point of BMI for the blacks. A different body mass index cut-off point had been advocated for the Chinese and Asians. This was consequent upon the fact that obesity does not directly correspond with diabetes in this group.
Chinese and Asians have higher waist circumference when compared with the whites61. The recommended WHO body mass index cut-off point of ≥ 23 kg/m2 for public health action for Asian represents increased risk and ≥ 27.5kg/m2 as high risk86. Crowther NJ and colleagues in a prospective cohort study in South Africa suggested waist circumference of ≥ 91.5cm as appropriate cut-off point for black women35, instead of ≥ 80cm advocated by International Diabetes Association (IDA).
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In this study, it was observed that blood glucose showed weak positive correlation with body mass index in the respondents which was not significant. Body mass index is expected to correlate with blood glucose positively. Bakari AG and colleagues in Zaria, Nigeria, observed positive correlation between BMI and random blood glucose among the females, but no correlation in the males91.
This study showed high degree of awareness of lifestyle modification with an average of about 70.6% of all subjects having knowledge of at least one lifestyle modification. A similar study in a rural population showed average awareness of 38.1% of subjects having knowledge of at least one lifestyle modification54. The difference could be attributed to difference in locality of the two populations. Generally rural population have low literacy level, Mumu SJ and colleagues observed that high literacy level (secondary School and above education) is independently associated with good knowledge and attitude of risk factors of diabetes63. The proportion of diabetics who were not aware of at least one lifestyle modification activity was higher 14.5%(10), unlike those who were aware of at least one lifestyle modification activity 8.5%(18).
Diabetes was more in the respondents who had ever smoked 12.0%(3). The odds of not having diabetes in smokers was low (OR = 0.311)[CI = 0.054-1.797). This effect was not significant (p = 0.192). Similarly in a community population study in Turkey, no significant association was found between cigarette smoking and diabetes 32. A community population study
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in South Africa did not find any significant association between diabetes and smoking 60. This was similar to finding in Inter Act consortium study82. The similarity with these studies could be similarity in study design, since they were all observational cross-sectional or case-control studies. However, a community population cohort prospective study in China observed that smoking more than 20 sticks of cigarette per day and a smoking duration of 40 years or more was associated with increased risk of diabetes69. The difference observed could be due to the prospective study design. Nicotine which is one of the active components of cigarette decreases insulin sensitivity, and causes disorder of glucose and lipid metabolism75. In a community population meta-analysis study, it was observed that current smokers had 45% increased risk of diabetes than non-smokers69. There seems to be a dose response relationship between risk of diabetes and the number of sticks of cigarette smoked69. In this study, the lack of association between cigarette smoking and diabetes may be due to the small number of the respondents that smoked (25). Again, none of the subjects in this study smoked 20 sticks of cigarette per day and none of the smoking index was significant.
In this study, diabetes was more prevalent in those that have ever consumed alcohol, in the responders 12.4%(19). There was no significant association between alcohol consumption and diabetes. Those who were heavy consumers had more diabetes 20.0%(1). The effect of alcohol consumption seems to increase the odds for diabetes in the responders (OR = 1.592)[CI
= 0.614-4.123] though the deduction is not strong that it is not significant because the confidence
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interval crossed one. Moderate alcohol consumption improves insulin sensitivity, increases HDL cholesterol and adinopectin, while heavy alcohol intake results in excess calorie intake and obesity, increased triglyceride and pancreatitis61. Some community population studies found significant association between alcohol consumption and diabetes. Light and heavy alcohol consumption increase risk of diabetes 68, 69. Similar to this study, Ekpenyong CE and colleagues in a community population study in Uyo, Nigeria and Aksu H and colleagues in a community population study in Bursa, Turkey did not find any association between alcohol consumption and diabetes 26,32. The similarity between Uyo study and this study could be due to similarity in the race of respondents.
Diabetes was commoner among those who were physically active. Physical activity did not have significant association with diabetes. Physical activity seemed to lower the risk of diabetes in the respondents (OR = 0.864)[CI = 0.915-1.063], this deduction is not strong because the confidence interval crossed one. This effect was not statistically significant (p = 0.573).
Using occupation to assess physical activity in this study was not the best. Energy expenditure or metabolic equivalents (METs) still remain the standard indices for assessing physical activity.
Using METs was not feasible in this study because very few subjects engaged in regular exercise. The non-association observed could be due to non-standard method used for assessing physical activity. However, physical activities have been shown to reduce the risk of diabetes by 58.0% in overweight prediabetic people. It improves glycaemic control and prevents
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cardiovascular diseases73. Inactivity encourages weight gain. Hu HB and colleagues in a community population study, observed that activities that reduce physical activity like modernization of mode of transportation, driving and time spent watching television have been associated with diabetes61. These are common practices in our environment. Aksu and colleagues in Turkey did not observe any association between physical activity and diabetes32.
There was weak positive correlation between 280 subjects’ RBG and average systolic blood pressure, which was statistically significant (rs = .161, p = 0.007). Diabetes was commoner in the hypertensive subjects of the respondents 12.4%(14). There was no significant association between blood pressure grouping and diabetes. Hypertension increased the risk of metabolic syndrome and insulin resistance. Moreover, some antihypertensive drugs are diabetogenic6. Giday A and colleagues in Ethiopia observed significant association between hypertension and diabetes59. Motola AA and colleagues in a community study in South Africa equally observed significant association between hypertension and diabetes60. However, Sukurai M and colleagues in a community population study in Japan did not observe any association between hypertension and other chronic diseases and diabetes67. This was similar to the findings of this study. Possibly the no association observed in this study could be due to use of Random blood glucose to screen for diabetes which might have resulted in fewer subjects being identified as newly diagnosed diabetics.
90 5.5 CONCLUSION
The prevalence of diabetes was high in this population both in the obese and non-obese though higher in the obese. There was no statistically significant difference between the prevalence of diabetes in the obese and the non-obese. The prevalence of known diabetics was relatively low in both groups, despite being a hospital population. The two independent associated risk factors in the respondents namely: family history of diabetes and age were both non-modifiable risk factors. Obesity (BMI) surprisingly did not show any association with diabetes in this study.
There was weak positive but statistically significant correlation of 280 subjects’ random blood glucose and WC, age, and average systolic blood pressure in the respondents. There is need to screen all adult hospital clients especially those who are 60 years and above, who have family history of diabetes for diabetes since these risk factors were statistically significantly associated with diabetes.
91 5.6 LIMITATIONS
1) This is a hospital-based study. Its findings may not be applied to the general population.
2) Cross-sectional study design has weakness of not being able to ascertain temporal relationship and the strength of recommendation is low
3) Use of random blood glucose for screening for abnormal blood glucose is not ideal as many with abnormal glucose could have been missed, unlike fasting glucose or glucose tolerance tests.
4) All potential confounders may not have been completely detected and accounted for and hence cannot be ruled out.
5) There was delay between the time of printing the questionnaire and collecting data due to financial constraint.