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| ORIGINAL ARTICLE |
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| Year : 2015 | Volume
: 18
| Issue : 5 | Page : 1-7 |
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Socio-demographic and Anthropometric risk factors for Type 2 diabetes in Maiduguri, North-Eastern Nigeria
ID Gezawa1, FH Puepet2, BM Mubi3, AE Uloko1, B Bakki3, MA Talle3, I Haliru4
1 Department of Medicine, Bayero University/Aminu Kano Teaching Hospital, Kano, (Formerly at the University of Maiduguri Teaching Hospital, Maiduguri), Nigeria
2 Department of Medicine, Jos University Teaching Hospital, Jos, Nigeria
3 Department of Medicine, University of Maiduguri Teaching Hospital, Maiduguri, Nigeria
4 Federal Medical Centre, Birnin Kudu, Jigawa state, Nigeria
| Date of Web Publication | 19-Jan-2015 |
Correspondence Address:
I D Gezawa
Endocrinology, Diabetes and Metabolism Unit, Department of Medicine, Bayero University /Aminu Kano Teaching Hospital, P.M.B. 3452, Kano
Nigeria
 Source of Support: None, Conflict of Interest: None  | Check |
DOI: 10.4103/1118-8561.149495
Background: The prevalence of type 2 diabetes (T2DM) is on the increase in developing countries due to urbanization, ageing population, physical inactivity and the high prevalence of obesity. Identifying the risk factors for T2DM is a necessary step in the planning of preventive measures to reduce the burden of the disease. The aim of this study was to determine the socio-demographic and anthropometric risk factors for type 2 diabetes in Maiduguri, northeastern Nigeria. Methods: We randomly selected 242 subjects resident in the study location. Trained interviewers obtained socio-demographic data from each respondent using a pretested questionnaire. Physical measurements for anthropometric indices were carried out using standard methods. Fasting blood samples were collected for the determination of fasting plasma glucose and diagnosis of diabetes. Data were analyzed using SPSS version 16 for windows and a P < 0.05 was considered significant. Results: All the 242 subjects recruited for the study responded. The mean (SD) age of 96 (39.7%) males and 146 (60.3%) females was 40.0 (14.6) years and 41.5 (12.0) years respectively, P > 0.05.The crude prevalence of diabetes was 17 (7.0%). The prevalence was 10(10.4%) in males and 7(4.8%) in females (M: F = 2.1:1), P < 0.05. The independent risk factors for diabetes identified were increasing age (OR = 3.72, 95% CI = 0.83-16.7, P = 0.007) and waist circumference in both males (OR = 1.65, 95% CI = 0.75-3.63, P = 0.036). and females (OR = 1.63, 95% CI = 0.56-4.78, P = 0.025). Conclusion: We identified increasing age and waist circumference to be the independent risk factors for T2DM in Maiduguri, Northeastern Nigeria. Keywords: Type 2 diabetes, risk factors, prevalence, Maiduguri, North-Eastern Nigeria
How to cite this article:
Gezawa I D, Puepet F H, Mubi B M, Uloko A E, Bakki B, Talle M A, Haliru I. Socio-demographic and Anthropometric risk factors for Type 2 diabetes in Maiduguri, North-Eastern Nigeria. Sahel Med J 2015;18, Suppl S1:1-7 |
How to cite this URL:
Gezawa I D, Puepet F H, Mubi B M, Uloko A E, Bakki B, Talle M A, Haliru I. Socio-demographic and Anthropometric risk factors for Type 2 diabetes in Maiduguri, North-Eastern Nigeria. Sahel Med J [serial online] 2015 [cited 2023 Mar 21];18, Suppl S1:1-7. Available from: https://www.smjonline.org/text.asp?2015/18/5/1/149495 |
| Introduction | |  |
Diabetes mellitus (DM) constitutes a major public health problem worldwide. In 2000, there were an estimated 151 million people with DM worldwide. [1] According to the International Diabetes Federation 366 million people had diabetes in 2011 with a projected increase to 552 million by 2030. [2] The developing countries are expected to be worst hit by the epidemic with an anticipated 170% increase in DM prevalence. Type 2 diabetes (T2DM) poses a major global health threat both in the developed and developing countries. [1] Factors responsible for the soaring epidemic of T2DM in the developing countries include urbanization, ageing population, physical inactivity and increasing obesity rates. [1],[3]
Nigeria has witnessed a significant rise in the prevalence of DM over the past two decades. In 1992, the crude prevalence of DM was 2.2% for Nigeria as reported by the National non-communicable diseases survey with Lagos mainland having the highest rate of 7.2%. [4] Drawing from information on the African continent, the International Diabetes Federation (IDF) estimates a current overall prevalence of 5% for DM in Nigeria. [2] This represents more than two-fold increase from the previously reported National standardized rate.
Maiduguri, a major city and the capital of Borno state in north-eastern Nigeria, is experiencing rapidly changing social and economic characteristics. The population of Maiduguri is gradually transiting to a more westernized pattern of lifestyle characterized by excessive consumption of calorie-dense foods and decreased physical activity. We have previously reported physical inactivity to be an independent risk factor for T2DM in Maiduguri metropolis. [5] To the best of our knowledge, there is paucity of recent data on the socio-demographic and anthropometric determinants of T2DM in North-Eastern Nigeria. In this study, we aimed to identify the socio-demographic and anthropometric risk factors for T2DM among adults in Maiduguri metropolis of North-Eastern Nigeria with a view to providing current data for purposes of planning health budgets by government and improving knowledge on the subject.
| Methods | | |
Two hundred and forty two consenting subjects aged 15-70 years were selected systematically between July and November 2009. Approval for the conduct of this study was granted by the Research and Ethics Committee of UMTH. Socio-demographic data were obtained from each respondent using a pre-tested questionnaire. Physical measurements for weight, height, waist and hip circumferences were carried out on each subject. Weights were recorded in kilograms (to the nearest 0.5kg) using a calibrated bathroom scale (Soehnle-Waagen GmbH and Co. KG, Wilhelm-Soehnle-Straße 2, D-71540 Murrhardt/Germany) positioned on a firm horizontal surface, with the subject clad in minimum clothing and without shoes. Height (in metres) to the nearest 0.1m was measured using portable locally manufactured stadiometers. Subjects stood erect, without shoes and headgears, on a flat surface with the heels and occiput in contact with the stadiometer. Body mass index (BMI) was calculated by dividing the weight (kg) by the square of the height in metres-squared (m 2 ).Waist circumference (WC) was measured to the nearest 0.1cm (with a non-stretchable dressmaker's tape) at a point mid-way between the margin of the lowest rib and the iliac crest. Hip circumference (HC) was measured at the horizontal level of maximum circumference around the buttocks (posteriorly) and the pubic symphysis (anteriorly) to the nearest 0.1cm. Waist-to-hip ratio (WHR) was calculated by dividing the WC (cm) by the HC (cm).
Definition of operational terms
- Diabetes Mellitus (DM)-was diagnosed as fasting plasma glucose > 7.0 mmol/L as recommended by WHO [6]
- Fasting- was defined as no calorie intake for at least 8 hours
- Body mass index (BMI) - was expressed in kg/m 2 . Subjects with BMI < 18.5 were classified as underweight and those with BMI of 18.5-24.9 were classified as having normal weight. Those with BMI of 25.0-29.9 and ≥ 30.0 were classified as overweight and obese respectively [7]
- Waist circumference (WC) - waist circumference ≥ 102 cm in males and ≥ 88 cm in females were considered abnormal [7]
- Waist to hip ratio (WHR) - waist-to-hip ratio of ≥ 0.90 in males and ≥ 0.85 in females were considered abnormal [7]
- Social status: The subjects in this study were grouped into five social classes according to the Registrar General's Classification [8]
- Age categorization: The study subjects were classified into three age brackets for the purposes of this work. These were young age (15- 34 years), middle age (35- 64 years) and elderly age (65- 70 years). Similar age categorization was used in a previous study. [9]
Whole blood samples (2.5 ml) were collected in fluoride-oxalate containers and transported to the Chemical Pathology department of UMTH in ice-containing improvised cold-chains within an hour of collection. The samples were centrifuged at 3000rev/min for 3 minutes and the plasma separated for the estimation of fasting plasma glucose (FPG) using the glucose oxidase method. [10] The intra-and inter-assay coefficients of variation (CV %) of the subjects' plasma glucose were 5.1% and 8.8% respectively.
The data collected were analyzed using SPSS for Windows (version 16.0; SPSS Inc. Chicago, Illinois, USA) statistical software package. Continuous variables were expressed as mean (SD). Multiple logistic regression analysis was used to assess the independent effects of the risk factors for type 2 diabetes. Odds ratio (OR) estimates were calculated separately and compared with non-diabetic group. A P < 0.05 was considered significant.
| Results | | |
All the 242 subjects selected participated in the study giving a response rate of 100%. There were 96 (39.7%) males and 146 (60.3%) females. The mean (SD) age of the study subjects was 39.4 (13.7). Male subjects had a mean age of 40.0 (14.6) years compared with 41.5 (12.0) years for the female subjects (P = 0.041). The fasting plasma glucose ranged from 2.2 - 12.0 mmol/L, with a mean of 4.8 (1.6) mmol/L. Male subjects had a mean FPG of 4.8 (1.3) mmol/L, compared with 4.8 (1.7) mmol/L for the females (P > 0.05). The clinical characteristics of the study subjects are shown in [Table 1]. | Table 1: Clinical characteristics of subjects selected for fasting plasma glucose estimation by gender
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Prevalence of diabetes among the study subjects
Overall, the crude prevalence of Diabetes Mellitus (DM) was 17 (7.0%). The prevalence was 10 (10.4%) in males and 7 (4.8%) in females (M: F =2.1:1) being significantly higher in males than in females, P < 0.05. Analysis by age group revealed a prevalence of 3 (3.3%) in the young age group, 13 (9.6%) in the middle age category and 1 (7.1%) in the elderly age group. The prevalence was higher in subjects ≥35 years, 14 (9.3%) than in subjects <35 years, 3 (3.3%). Of the seventeen detected diabetics, nine were previously undiagnosed giving a prevalence of 3.7%.
Socio-demographic and anthropometric risk factors for T2DM among the study subjects.
Age and the prevalence of diabetes
The prevalence of DM increased with age in both sexes (X 2 = 12.65, P < 0.001) as shown in [Figure 1].Two of the subjects found to have diabetes, both males were within the young age bracket (23 and 26 years respectively). These subjects had BMI of 25 kg/m 2 and 28 kg/m 2 respectively. The prevalence of DM was most pronounced in the middle age category in both sexes. The mean (SD) age of subjects with diabetes was 44.4 (12.5) years, compared with 36.2 (14.4) in subjects without diabetes, P < 0.001 and was similar in males (41.5 ± 12.4 years) and females (48.5 ± 12.2 years), P > 0.05. There were no subjects with diabetes detected in the young age category among females. None of the elderly male subjects was found to have diabetes. | Figure 1: Prevalence of Diabetes mellitus by age in the study subjects. The prevalence of diabetes increased with age and was highest in the middle age category
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Body mass index and the prevalence of diabetes
The prevalence of DM increased across BMI ranges in both sexes (X 2 for linear trend = 4.72, P < 0.029), as shown in [Figure 2].The mean (SD) BMI of subjects with diabetes [27.8 (4.1) kg/m 2 ] was significantly higher than that of subjects without diabetes [24.7 (5.3) kg/m 2 ] in both sexes (P < 0.05). Male subjects with diabetes had significantly higher mean BMI [28.4 (4.4) kg/m 2 ] than females with diabetes [26.9 (3.8) kg/m 2 ], P < 0.05. Diabetes was not detected among subjects with BMI below 18.5 kg/m 2 . | Figure 2: Prevalence of Diabetes mellitus according to body mass index in the study subjects. The Prevalence of diabetes increased across the BMI ranges. Diabetes was not detected in subjects with BMI < 18.5 kg/m2
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Waist circumference and the prevalence of diabetes
The prevalence of DM increased across WC quartiles in both males (X 2 = 17.27, P = 0.016) and females (X 2 = 7.50, P = 0.024) as shown in [Figure 3]. The mean (SD) WC of subjects with diabetes, 98.8 (13.8) cm was higher than that of subjects without diabetes, 82.1 (12.6) cm. The difference was statistically significant (P < 0.05).Although females with diabetes had a higher mean (SD) WC compared with their male counterparts [99.3 (14.6) cm vs 98.3 (13.9) cm], the difference was not significant (P > 0.05). The prevalence of DM was significantly associated with increasing WC (X 2 = 45.14, P < 0.001). | Figure 3: Prevalence of Diabetes mellitus according to quartiles of Waist circumference among the study subjects by sex. The prevalence of DM increased across the quartiles, from the lowest (1st quartile), to the highest (4th quartile). Waist circumference values greater than the 4th quartile (≥93cm in males and ≥95 cm in females) were considered abnormal
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Waist-to-hip ratio and the prevalence of diabetes
The prevalence of DM increased across quartiles of WHR [X 2 = 9.30, P < 0.05 for males and X 2 = 13.29, P < 0.05 for females as shown in [Figure 4].The mean (SD) WHR of subjects with diabetes, 0.94 (0.07) was higher than the mean (SD) WHR for normal subjects, 0.89 (0.09). The difference was statistically significant, P < 0.05.Males with diabetes had higher mean (SD) WHR compared with their female counterparts [0.95 (0.05) vs 0.93 (0.09), P > 0.05], the difference was however, not statistically significant. | Figure 4: Prevalence of Diabetes mellitus according to Waist hip ratio by sex of study subjects. The prevalence of DM increased across the quartiles, from the lowest (1st quartile), to the highest (4th quartile). Waist hip ratio values greater than the 4th quartile (≥0.95 in males and ≥0.93 in females) were considered abnormal
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Family history and the prevalence of diabetes
Only two of the subjects detected as having diabetes had family history of diabetes. Of these, one had a diabetic mother, the other a diabetic father. Among subjects without diabetes, 17 (7.6%) had family history of diabetes. There was no association between family history and prevalence of DM (X 2 = 0.07, P = 1.000).
Social class and prevalence of diabetes
The prevalence of DM was highest in social class V, 41.2% (7 out of 17) while it was roughly equal in social classes I, III and IV at 17.6% each. The prevalence of DM was lowest in social class II. The prevalence of DM was not significantly associated with social status (X 2 = 7.06, P > 0.05).
Independent effects of risk factors for diabetes in the study subjects
Multiple logistic regression analyses were performed to assess the independent effects of the risk factors for type 2 diabetes. Odds ratio (OR) estimates for DM were calculated separately and compared with non-diabetics. Increasing age and WC were found to be independently associated with DM [Table 2]. | Table 2: Odds ratio estimates of socio-demographic and anthropometric risk factors for diabetes mellitus (type 2DM)
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| Discussion | | |
The prevalence of DM is on the increase globally especially in the developing countries. 1 In this study we found an overall prevalence of DM of 7.0% among our subjects. This is higher than the report from previous population based surveys in Nigeria. [11],[12] The overall prevalence rate from this study is however, lower than the 19.3% reported by Johnson, [13] although his subjects were much older (age range 44-99 years).
Strict comparisons of the prevalence rates of DM from this study with those of these Nigerian studies are rather difficult to make because of the differences in criteria for diagnosis employed and the age range of the subjects in the various studies. In the study by Erasmus et al., [12] diagnosis was based on 1980 WHO criteria [14] and subjects with fasting glucose values between 6.5 and 7.7 mmol/L underwent oral glucose tolerance testing. Their subjects were also aged 10 years and above. Ohwovoriole et al. [11] on the other hand used casual plasma glucose and subjects below 15 years of age were also tested.
The male preponderance among identified diabetics in this study concurs with the findings of Puepet [15] in a study of the prevalence of DM in Jos, north-central Nigeria. Some surveys reported higher rates in females. [11],[12],[16] The lower proportion of elderly women among subjects found to have diabetes in this study may explain this finding since the prevalence of DM is known to increase with age. More than half of our detected diabetics were previously undiagnosed. This is comparable to the observation of Oyegbade et al. [17] who found 50% of their identified diabetics to have been previously undiagnosed.
Socio-demographic and anthropometric risk factors for diabetes in the study subjects
Age, sex and the prevalence of DM
The increasing prevalence of DM with age observed in this study is in agreement with previous findings. [18],[19] It also confirms the observations of Ohwovoriole et al. [11] and Erasmus et al. [12] in Nigeria who observed that the prevalence of DM rises with age. In this study, the rise in prevalence of DM was most pronounced after 35 years (in both sexes). Puepet [15] reported a rise in prevalence of DM after 55 years of age in his study subjects. Age >35 years was identified as an independent risk factor for DM in this study. Levitt et al. [18] in South Africa reported similar finding. Worsening of insulin resistance with age, decreased physical activity and longevity of diabetes patients due to improved care were the reasons given for the rising prevalence of DM with age. [20],[21] Although, the prevalence of DM was higher in males than in females in this study, gender was not found to be an independent risk factor for DM.
Body mass index and prevalence of DM
The prevalence of DM in this study increased across BMI groups similar to the findings of Puepet. [15] The mean BMI of detected diabetics in this study was higher than that of non-diabetics. This is in keeping with the observations of Nyenwe et al, [22] who also reported a higher mean BMI among their detected diabetics compared with normal subjects. However, BMI in this study was not found to be an independent risk factor for DM, contrary to the findings of Oyegbade et al. [17] The preponderance of females compared to males among detected diabetics in the latter study, may explain their finding, since females are known to have higher mean BMI than males as demonstrated in the index study.
Waist circumference and the prevalence of DM
Prior studies have suggested that WC is a better predictor of cardiovascular disease and type 2 diabetes than are BMI and WHR. [23] Okosun et al, [24] in a study of 5,042 men and women 25-74 years of age from Nigeria, Jamaica and the U.S., showed that increasing WC quartiles were significantly associated with risks of hypertension and type 2 diabetes in the three populations. In this study, the prevalence of diabetes also increased with increasing WC quartiles and WC was found to be an independent risk factor for diabetes among our subjects. This finding supports the fact that abdominal fat localization, as measured by WC, is more important than the total amount of body fat in predicting the risk of DM.
Waist hip ratio and the prevalence of DM
The prevalence of DM increased across WHR quartiles in this study. The identified diabetics in this study had higher mean WHR than subjects without diabetes. High WHR was however, not found to be an independent risk factor for DM in this study. Although WHR measures central fat deposition, it is a poor measure of visceral fat mass, particularly in lean individuals. In addition, we have previously reported WHR to correlate poorly with measures of insulin resistance than either WC or BMI in our setting. [25] This is contrary to the findings from other studies that identified increased WHR as a better marker of insulin resistance and DM than WC or BMI. [26],[27]
Family history and the prevalence of DM
Type 2 diabetes is known to show a strong familial aggregation. [28] Only two (11.8%) of the detected diabetics in this study had positive family history of DM. Studies of family history of DM in Africans have mostly been on probands with DM. [29],[30],[31] Osuntokun et al. [30] found family history of DM only in 2.4% among Nigerian their subjects with diabetes. Puepet et al[28] on their part reported positive family history of DM in 23.8% of 150 probands with diabetes. Interestingly in this study one of the identified diabetics with a positive family history had a mother with DM while the other had a father with the disease. This is in keeping with reports from other studies. [6],[32] Other studies have however, reported excess female associations. [33],[34] The association between family history and DM in this study was poor and insignificant (X 2 = 0.07, P = 1.000). Family history, as a risk factor for DM in this study was based on direct questioning of the respondents. People in our environment are often reluctant to reveal family history of illnesses. Only a few are well informed about diabetes mellitus. These and other yet unidentified factors may be responsible for the low prevalence of family history among identified diabetics observed in this study.
Social class and prevalence of DM
The prevalence of diabetes mellitus has been associated with low socioeconomic status in developed countries, with an inverse relationship in developing countries. [35] The highest prevalence of DM in this study was seen in the low social class. This is in agreement with the findings of the non-communicable diseases survey in Nigeria that reported a higher prevalence of 4.6% in the low social class, compared with 2.5% in the high social class. [4] However, Nyenwe et al. [22] reported a higher prevalence of DM in the high social class among their subjects. Individuals in the low socioeconomic strata of the society are more likely to be less knowledgeable about preventive measures for medical conditions such as DM and to have limited access to healthcare. These and other yet unidentified factors may be responsible for the higher prevalence of DM among subjects in the low social class as demonstrated in this study. Multivariate analysis however, failed to identify social class as an independent risk factor for DM in this study.
The limitations of our study include the relatively small sample size employed, which makes generalization of our findings difficult, the cross-sectional design of the study and the fact that family history as a risk factor for type 2 DM was based on direct questioning of the respondents only. This alone without considering other first-degree relatives may not be enough to determine genetic influence in DM.
| Conclusion | | |
The prevalence of type 2 diabetes in Maiduguri metropolis is high. The finding of a high prevalence of undiagnosed DM in this study, calls for active screening of people for DM in the metropolis. Increasing age and waist circumference were found to be the independent risk factors for type 2 diabetes in the metropolis. Since central adiposity, as detected by measuring waist circumference, is potentially modifiable through lifestyle changes, concerted effort should be made to promote healthy lifestyle among the populace to reduce the burden of type 2 diabetes in Nigeria.
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[Figure 1], [Figure 2], [Figure 3], [Figure 4]
[Table 1], [Table 2]
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| Oluwaseun S. Ojo,Ademola O. Egunjobi,Adefemi J. Fatusin,Bolatito B. Fatusin,Odunola O. Ojo,Farouq A. Ololade,Patience A. Eruzegbua,Oluseyi A. Afolabi,Ayomiposi A. Adesokan | | South African Family Practice. 2020; 62(1) | | [Pubmed] | [DOI] | | | 5 |
Alteration of hemostatic and hemorheological parameters in Nigerian patients with type 2 diabetes mellitus: Does age play a role? |
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| Momodu Imoru,Jessy Medugu,Rebecca Gali | | International Journal of Medicine and Health Development. 2020; 25(2): 77 | | [Pubmed] | [DOI] | | | 6 |
Correlation of anthropometric indices with fasting blood glucose and blood pressure among university students in Kano, Nigeria |
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| IsyakuMukhtar Gwarzo,MabarukaNasir Adam,NafisaYusuf Wali,SalisuAhmed Ibrahim | | Nigerian Journal of Basic and Clinical Sciences. 2020; 17(2): 128 | | [Pubmed] | [DOI] | | | 7 |
Risk Factors of Thyroid Dysfunction in Patients With Type 2 Diabetes Mellitus |
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| Stanley U. Ogbonna,Ignatius U. Ezeani | | Frontiers in Endocrinology. 2019; 10 | | [Pubmed] | [DOI] | | | 8 |
Prevalence and Risk Factors for Diabetes Mellitus in Nigeria: A Systematic Review and Meta-Analysis |
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| Andrew E. Uloko,Baba M. Musa,Mansur A. Ramalan,Ibrahim D. Gezawa,Fabian H. Puepet,Ayekame T. Uloko,Musa M. Borodo,Kabiru B. Sada | | Diabetes Therapy. 2018; 9(3): 1307 | | [Pubmed] | [DOI] | | | 9 |
Prevalence of type 2 diabetes mellitus in Southern Cross River: a cross-sectional observational survey |
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| Akaninyene Otu,Margaret Akpan,Emmanuel Effa,Victor Umoh,Ofem Enang | | International Journal of Diabetes in Developing Countries. 2018; | | [Pubmed] | [DOI] | |
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