ABSTRACT
The study assessed diabetic risk in an apparently healthy young adult population. One hundred and sixty-five (165) subjects: males-90(54.6%), females-75(45.4%) aged 18-30years were recruited for the study. International Diabetic Federation (IDF) structured questionnaires were used in the study. Anthropometric indices, such as height, weight, waist circumference (WC); biochemical indices, such as fasting blood glucose (FBG), and physiological parameters {blood pressure (BP)}, were measured using standard procedures. Body mass index (BMI) was calculated and Diabetic risk scores predicted using IDF risk calculator (IDFRISC). Glycated haemoglobin (HbAlc), and C-Reactive proteins (CRP) were determined in a subset (n=50) of the subject population representing 30.4% and used for further diabetic risk predictions based on the United Kingdom diabetes index and American Heart Association/Center for disease control index (AHA/CDC). Demographic distribution of the subjects was as follows: mean ages of females and males were 22.6±3.7 and 22.9±3.4yrs respectively. Result for anthropometric indices showed that mean height(cm) was significantly (p≤0.05) lower in females (164.7±6.5) than males (176.8±78), while mean body weight (kg) had no significant difference between the sexes, females (67.6±9.7) and males (69.3±9.5) . There was also no significant (p≥0.05) difference in mean BMI; (females 24.7±2.9 males; 22.1±2.4) and mean waist circumference (females 77.6±7.9; males75.7±5.9cm) across the sexes. Fruit consumption pattern among the sexes showed that females consumed a significantly (p<0.05) higher (0.39±0.49) amount of fruits than males (0.20±0.40). Level of physical activity (females -0.53±0.50; males 0.52±0.59), mean blood pressure {systolic-females122.7±12.7) and males (127.9±10.94)} and diastolic –female (74.89±5.03) and males (75.33±5.02), mean FBG (mg/dl) in females(62.6±11.26) and males(62.6±10.3) showed no significant (p>0.05) difference across the sexes. Mean HbAlc (8.81± 0.43) and CRP(females-1.19±0.58 and males -1.92±0.67 ) also showed no significant difference (p>0.05) between the sexes. The mean calculated risk score points for females (6.13±3.9) was significantly (p<0.05) higher than for males (4.26±3.30). Based on the IDFRISC 110 subjects (66.6%) had a low risk of diabetes, 27subjects (16.4%) had a slightly elevated risk of diabetes, another 27 subjects (16.4%) had a moderate risk of diabetes, while 1 subject (0.6%) was at a high risk of diabetes. Using the UK diabetic index for HbAlc, 20subjects were normal, 8subjects were pre-diabetic and 22subjects were observed to be diabetic, while based on the AHA/CDC index for CRP 10 subjects were normal, 40 subjects were found to be pre-diabetic and none of the subjects was observed to be diabetic. These findings when analyzed alongside family history obtained from questionnaires suggested that risk of diabetes mellitus is higher in young female adults than males, heritable and requires a deliberate and concerted screening effort to identify populations and individuals at risk of developing the disease.
TABLE OF CONTENTS
PAGE
Title
page i
Declaration ii
Certification iii
Dedication iv
Acknowledgment
v
Table
of contents viii
List
of tables x
List
of figures xi
Abstract
xii
CHAPTER 1: INTRODUCTION
1.1 Background of the Study 4
1.2 The Clinical Algorithm for DM 8
1.2 Aim of the Study 9
1.3 Objectives of the Study 9
1.4
Justification for the Study 10
1.5
Statement of Problem 11
CHAPTER 2: LITERATURE
REVIEW
2.1
Epidemiology of Diabetes Mellitus 13
2.2
The Biochemistry of Diabetes 18
2.3 DM Treatment and Management 24
2.4
Epigenetics and Susceptibility to Diabetic Gene 26
2.5
Genetic Markers for Diabetes Mellitus 29
2.6
Diabetes and the Risk Assessment Using the Diabetes Risk Score 31
2.7
Some Tools Used In Calculating Diabetic Risk Assessment 32
2.8
The IDFRISC Diabetes Risk Calculator 39
2.9
FINDRISC Diabetes Risk Calculator 39
2.10
The INDRISC Diabetes Risk Score Calculator 36
2.11
Performance of Diabetes Risk Scores 44
2.12.
Biochemical Indices/Biomarkers for Diabetic Risk Assessment 45
2.12.1
Role of haematological indices 45
2.12.2 Role of anthropometric factors/parameters 55
2.12 Diabetes Assessment and Consequences 57
CHAPTER 3: MATERIALS AND METHODS
3.1
Materials/Instruments 58
3.2
Kits 58
3.3
Reagents, Chemical and Solutions
59
3.4 Storage and
stability of reagents 60
3.5
Specimen Collection and Preparation 61
3.6
Methods and Measurements 61
3.7
Estimation of Total Haemoglobin (Hb) 63
3.8
Estimation of Glycosylated Haemoglobin/Glycated Haemoglobin
Glycohaemoglobin (HbAlc)
64
3.8.1
Principle 64
3.8.2
Hemolysate preparation 64
3.8.3
Glycohemoglobin preparation 64
3.9
Estimation of Total Hb fraction 65
3.9.1
Procedure 65
3.10
Estimation of C – Reactive Protein 65
3.10.1
Test procedure 66
3.11
Data Analysis 67
CHAPTER 4: RESULT AND DISCUSSION
4.1
Results 69
4.2
Discussion 91
CHAPTER
5: CONCLUSION AND RECOMMENDATION
5.1 Conclusion 107
52
Recommendations 107
5.3
Areas for Further Research 109
References 114
Appendices
LIST OF TABLES
Page
2.1 Some tools used in diabetic risk
assessment .
33
2.2: 10year risk prediction of developing DM
based on the FINDRISC Score model
36
2.3: Classification of Risk Factors and
their Risk scores 38
4.1: Key characteristics distribution of
the international diabetes risk calculator and some hematologic indices. 61
4.2 Distribution of risk factors in the normal
healthy adult population (n=165) based on the IDF diabetic risk calculator 63
4.3: Table of Means for the Haematologic
distribution between Male (n=27) and Female (n=23) Subjects (n=50). Aged
18-30yrs 65
4.4: Diabetes Care United Kingdom rating of
percentage glycated haemoglobin and their equivalence to mg/dl with respect to
DM 46
4.5: CRP prediction for diabetes according to
the American Heart Association (AHA) and the Centre for Disease Control (CDC),
2018 49
4.6: Prevalence of DM risk using glycated
haemoglobin (HbAlc) in male and female subjects aged 18-30yrs. 66
4.7
Prevalence of DM risk using CRP in the male and female subjects aged 18-30yrs 67
4.8: Demographic distribution of % incidence
Rate, of Hematologic data in male and female subjects. 68
LIST OF FIGURES
Page
4.1 Relative mean distribution of age (years)
for the males, females and total respondents from the population based on the
IDF risk calculator. 69
4.2: Relative mean distribution of height (cm)
for the males, females and total respondents from the population based on the
IDF risk calculator. 70
4.3
Relative mean distribution of body weight (kg) for the males, females and total
respondents from the population based on the IDF risk calculator. 71
4.4 Relative mean distribution of BMI (kg/m2)
for the male, female and total respondents from the population based on the IDF
risk calculator. 72
4.5 Relative mean distribution of waist
circumference (cm) for the males,
females
and total respondents from the population based on the IDF risk calculator. 73
4.6 Relative mean distribution of fasting
blood glucose (mm/dl) for the males, females and total respondents from the
population based on the IDF risk calculator. 74
4.7 Relative mean distribution of physical
activity for the males, females and total respondents from the population based
on IDF risk calculator. 75
4.8 Relative mean distribution of family
history for the males, females and total respondents from the population based
on the IDF risk calculator. 76
4.9 Relative mean distribution of daily
fruit/vegetable intake for the males, females and total respondents from the
population based on the IDF risk calculator. 77
4.10 Relative mean distribution of systolic
blood pressure for the males, females and total respondents from the population
based on the IDF risk calculator. 78
4.11 Relative mean distribution of diastolic
blood pressure for the males, females and total respondents from the population
based on the IDF risk calculator. 79
4.12 Relative mean distribution of calculated
risk score (points) for the male, female and total respondents from the
population based on the IDF risk calculator 80
4.13 Mean relative distribution of C-reactive
protein CRP (mm/dl) for the males, females and total respondents from the
population based on the hematologic assessment. 81
4.14 Relative mean distribution of glycated
haemoglobin. HbAlc(%) for the male, female and total respondents from the
population based on hematologic assessment. 82
4.15
Relative mean distribution of Total haemoglobin, Hb(mm/dl) for the males,
females and total respondents from the population based on hematologic
assessment. 83
CHAPTER 1
INTRODUCTION
The World Health
Organization (WHO), describes Diabetes mellitus (DM) as a metabolic disorder with
heterogeneous aetiologies including chronic
hyperglycaemia and disturbances in the metabolism of carbohydrate, fat and
protein resulting from defects in secretion of insulin activity, action of insulin, or a combination of both events in
the body (WHO,1999a). The relative specific
effects of diabetes on the long-term include microvascular complications such
as the development of retinopathy in the eyes, nephropathy in the kidney and
neuropathy in the peripheral nervous system due to the atherosclerotic plague
in the vasculature supplying blood to heart, eyes, brain, limbs and kidney at
an early stage, and a complete obstruction of these vessels with increased
risks of myocardial infarction (MI), stroke, claudication and gangrene at a
later stage (Hanssen et al., 1992
and Brownlee, 2005). People suffering from
diabetes are susceptible to increased risk of cardiac, peripheral, arterial and
cerebrovascular disorders (Fox et al., 2007 and Abdul-Ghani et.al., 2009).
Diabetes and less forms of glucose intolerance, impaired glucose tolerance
(IGT) and impaired fasting glucose (IFG), does not only persist amongst persons
of diverse ethnicity but can also be found in almost every population in the
world now, and epidemiological evidence suggests that, without effective
prevention and control programs, the global burden of diabetes is likely to
continue to increase unabated (Zimmet et al., 2001; Alberti et al.,
2007 and Abdul-Ghani et.al., 2008). Diabetes affects many people
in the work force, with a major deleterious impact on both individual and
national productivity. As a result, Diabetes and its complications now confer a
socio-economic consequence which has a serious negative impact on the economies
of developed and developing nations of the world (WHO, 2016). It was against this background
that on 20th December, 2006, the United Nations General Assembly
unanimously passed Resolution 61/225 that declared diabetes an international
public health issue and also declared
14th November World Diabetes Day as an official Day to commemorate DM by
United Nations and her numerous member-countries. Several guidelines for the
diagnosis of diabetes have been published by WHO since 1965 till date. In 1991, Both diagnosis and classification was reviewed and
was published as the guidelines for the Definition, Diagnosis and
Classification of Diabetes Mellitus (WHO, 1999b). Glycated haemoglobin
(HbA1c) and its potential utility in diabetes care was first mentioned in the
1985 WHO report (WHO, 1985). As more relevant information needed for the diagnosis of
diabetes became available, WHO, in collaboration with the International
Diabetes Federation (IDF), convened a meeting of joined experts in 2005 to
review and update the recommendations on diagnosis only (WHO, 2006). After
consideration of the available data and the recommendations made at that time
by other international and global organisations, the following recommendations
was made by the 2005 consultation (WHO, 2016):
1.
The previous WHO diagnostic criteria should not be changed (WHO,
1999b).
2.
The diagnostic cut-point
for IFG (6.1 mmol/l; 110 mg/dl) should not be changed.
HbA1c should not be adopted as a diagnostic
test, as the challenges of accuracy in its measurement out-weighed the
convenience in its usage. WHO convened a second consultation meeting of joined
experts in order to update the 1999 and 2006 reports in March 2009, for the
inclusion of HbA1c in diabetes diagnosis based on available evidence (WHO,
2016). However, the American Diabetes Association (ADA) modified the WHO and IDF recommendations of
2016 on Diabetes definition, diagnosis and classification with new standards for medical care in
diabetes defined (Diabetes care, 2016; and ADA, 2010b). Some of the 2016 ADA
recommendations included giving treatment to vulnerable populations with
diabetes like persons with food insecurity, cognitive dysfunction or mental illness and Human immune Virus (HIV)
as well as considered the differences associated with ethnicity, socioeconomic
status, culture and gender in diabetes (Diabetes care, 2016).The 2016
recommendations declared all diagnostic test for diabetes effective and non-discriminatory
and stated clearly that no diagnostic test e.g fasting plasma glucose, 2-h
plasma glucose after a 75-g oral glucose tolerance test and glycated
haemoglobin is preferred over another for diagnosis as all are effective and
can be applied without discrimination to any intended population (Diabetes
care, 2016). It also ratified the relationship between age, BMI, risk of type 2
diabetes and prediabetes. Based on this ratification, the recommendations is
now to test adults beginning at age 45years regardless of their weight or BMI,
including asymptomatic adults and adults with monogenic syndromes (Diabetes
care, 2016). The use of information communications technologies (ICT) such as
diabetic apps and text messaging, good nutrition, vaccination, bariatric surgery
for those obesse and overweight were also recommended to affect lifestyles
(Diabetes care, 2016). The terms cardiovascular diasease (CVD) was replaced
with atherosclerotic cardiovascular disease (ASCVD), nephropathy was replaced
with diabetic kidney disease, retinopathy was replaced with diabetic
retinopathy and neuropathy was replaced with diabetic neuropathy as these were
more specific terms associated with diabetes with better emphasis (Diabetes
care, 2016). Also, for children and adolescents, the previous recommendation
was to obtain lipid profile at 2years of age, the updated recommendation is now
to obtain the lipid profile at 10years of age (Diabetes care, 2016). Also for
pregnant and lactating mothers with diabetes, the previous recommendation for glycated
haemoglobin of <6% (42mmol/mol) has been changed to a target of 6-6.5%
(42-48mmol/mol). This is to either tighten or relax the test sensitivity
depending on the hypoglycaemic risk status (Diabetes care, 2016). Furthermore,
glyburide used in gestational diabetes mellitus was recommended to be
deemphasized in all medical care procedure and report because recent evidence
now suggests it may be inferior to insulin and metformin in diabetes management
(Diabetes care, 2016). The members of the consultation for ADA/WHO diabetes
recommendations included experts in medicine, surgery, pharmaceutics,
diabetology, biochemistry, immunology, genetics, epidemiology and public health
(Schunemann et al., 2008).
1.1 BACKGROUND OF THE STUDY
The ADA has established DM as a
complex chronic illness that required routine medical care with a need for
risk-reduction by a set of multifactorial strategies that would ultimately lead
to glycaemic control (Diabetes Care, 2016). Also, the ADA has reported a
growing number of subjects to Diabetes (ADA, 2016). Therefore, education of
patient self-management and support are critical in prevention of acute
complications and will make the reduction of the risk of chronic complications
from diabetes possible (Diabetes Care, 2016). Hence, the need to deploy
predictive tools and standard medical care to monitor and control the growing
pattern and distribution of chronic diseases such as diabetes in any given
population overtime (Diabetes Care, 2016; and ADA, 2010a). In Medicine,
clinical risk of a disease can be calculated using predictive tools. Anderson et
al., (1991); defined as “the possibility, chances or probability of
developing a disease state in a given time or period either at present or
future". Within the clinical setting, the diabetes risk score calculators
as a predictive tool, have contributed important success in individual patient
disease treatment, management and prevention (Hippisley-cox et al.,
2007). The application of predictive tools to populations can give an insight
into the role played by risk factors on the future burden of diabetes mellitus
in an entire region or nation and further show the urgent need of interventions
and screening programs at the population level (Wild et al., 2004). At the
population level, clinical algorithms have been applied for diabetes mellitus
(Manuel et al., 2005 and Manuel et.al., 2010), but with
considerable challenges. Clinical risk tools require clinical data that are not
readily available at the population level, and for Diabetes mellitus(DM),
several Risk Scores exist (Eddy and Schlessinger, 2003) such as the American
Diabetes Risk Score (Selph et al.,
2015), the Australian Diabetes Risk Scores (Chen et al., 2010), the
atherosclerosis risk in communities (Schimdt et al., 2005), the
Cambridge Diabetes Risk Scores (Griffin et al., 2003), the Finnish
Diabetes Risk Score; FINDRISC (Lindstrom et al., 2010), the Indian
Diabetes Risk Score INDRISC and
Framingham Risk Score (Anderson et al., 1991 and Fox, and Sorlie, 2007) etc. These risk scoring tools
require clinical data that are usually collected infrequently or not at all at
the population level, as fasting blood sugar (Eddy and Schlessinger, 2003). In addition they are applicable
only to specific population, such as those with co-morbid conditions and or
those in specific age ranges (Stern et al., 2009). For a population
algorithm, the input variables should represent the entire population. Thus, it
should be population-based for meaningful use by health policy makers,
accessible to a wide audience, and regularly collected to update estimates. The
creation and application of a population-based algorithm for diabetes is
realistic and achievable, because the risk factors for diabetes are popular and
common, thus it can be measured in population health surveys through
self-reported questionnaires. (Rosella et al., 2009). Type 2 diabetes is
associated with increased risk of cardiovascular disease and premature
mortality, and morbidity, such as blindness, kidney failure and non-traumatic
amputations from microvascular complications (Li et al., 2008) as well
as hyperosmolar coma. The prevention and delay of the onset of diabetes using
lifestyle modifications that primarily target weight loss or therapeutic
pharmaceutical interventions has been demonstrated in randomized trials
(Lindstrom et al., 2006). This prompted numerous countries to enforce
diabetes screening, intervention and prevention programs nationwide (Colagiuri et
al., 2010). As a result of this national advocacy programs by these
countries guidelines that helps to evaluate and access for early diabetes intervention and subsequent
prevention was developed (Paulweber et al., 2010). Furthermore,
alternative methods of identifying individuals at high risk of developing
diabetes are needed especially in developing countries of Africa particularly
Nigeria (Buijsse et al., 2011 and Abdul-Ghani et.al., 2007). To
reduce cost, individual-level intervention programs are needed to typically
target high risk persons who have a greater chance of developing diabetes when
they are still in their normal states of health (WHO, 2006). To date, diabetes
prevention trials and screening included persons with impaired glucose
tolerance, who can be identified only by conducting an oral glucose tolerance
test (WHO, 2006). Massive population
screening, using the oral
glucose tolerance test method, may be
less feasible in identifying persons who might be at high risk or benefit from health promotion
interventions by either government,
corporate or individual sponsored screening for diabetes especially in
developing countries of Africa (Noris et al., 2008) such as Nigeria
where not much community-based sensitisation, public awareness and insufficient
funding is given to non-communicable chronic diseases such as diabetes mellitus
(Ejike et al., 2010; Ijeh et al., 2010a). Also, it appears
diabetes is even increasing amongst the educated members of the society meaning
that conventional education is not a criteria to be risk-free from DM. This was
observed by Odenigbo and Osuu (2012) that out of a sample of 197 diabetic
patients aged 30+ years, 52% had tertiary education, 27% had normal weight, 57%
and 12.5% were obese and overweight respectively. They also observed that the
relationship between Knowledge, Attitude and Practice (KAP) with nutritional status was not significant
(p>0.05). The concluded that their is need for diabetes education since a
large subgroup of Nigerians have poor nutritional status despite their high
educational background (Odenigbo and Osuu, 2012). Nyenwe et al., (2003)
when working on type 2 Diabetes risk in Port-Harcourt observed that out of 502
subjects aged 40+ years, 34 (6.8%) subjects were diabetic, of this subgroup
(n=34), 14 (41.2%) of them were unaware of their risk status, and the
remaining 20 (58.8%) were aware and a total of 83.7% of the subjects diabetic
were asymptomatic. They concluded that prevalence of type 2 diabetes in port-Harcourt
was relatively high. In Nsukka, Anoshirike et al., (2019) having assessed
1470 staff of the University of Nigeria (UNN) aged 25-70 using a multistage
sampling technique, observed that 85.4%
skipped breakfast, 43.1% exercised, 37% were overweight, 6.7% were obese
and 21.4% males as well as 100.0% females were at risk of cardiovascular
diseases. They concluded that poor dietary and lifestyle pattern, high
prevalence of overweight and obesity, risk of cardiovascular diseases among the
staff of the university of Nigeria campus. In Umudike a sample of 189 subjects,
90 being males and 99 being females aged 22-84 years was studied by Ijeh et
al., (2010b) and the authors observed a varying glycaemic and blood
pressure phenotypes in Umuahia. They implicated non-esterified fatty acids
(NEFA) in the risk of DM with the highest prevalence in those with high
cholesterol especially those with diabetes and hypertension (DH; 48.1%),
followed by those who were normoglycemic but hypertensive (NH;46.7%), and went
on to conclude that Dyslipidaemia is a surrogate risk to DM and High blood pressure
( Ijeh et al., 2010c) . Therefore, given the enormity of diabetic health
challenges in Nigeria evidenced from these studies, mass population-based
screening is urgently needed. However, screening targeted at populations with
high risk of diabetes (such as Nigeria) using the oral glucose tolerance test
is often cumbersome with higher cost, nonetheless, the yield and economic
efficiency of the assessment would probably be increased (Noris et al.,
2008). Thus, finding simpler, non-evasive, easy-to-use, and more pragmatic
methods that is cheap, less-burdening and would cover a larger population
faster in lesser time with high precision and acceptable accuracy is desirable.
This is geared towards identifying those at high risk of DM or those with an
onset cum progression to diabetes. Also, they will benefit from such targeted
prevention programs in Nigeria and other developing countries with a paramount
goal of controlling a possible pandemic (Lindstrom et al., 2010). This
understanding and proactive action against diabetes is important towards
delaying the outbreak of a pandemic to the disease in future, and the urgency
of this in contemporary lifestyle modifications and adjustment to healthy
living cannot be over-emphasized (ADA, 2006). Furthermore, it is noteworthy
that there are several risk factors and their corresponding risk score
calculators for Diabetes. Some of these are highlighted in table 2.3 of this
study. Risk score calculators used in DM studies use varying distribution of
risk factors depending on the study population. In fact no two risk score
calculators have exactly same risk factors. Some of these risk score
calculators and populations were it was applied are highlighted in table 2.1 of
this study. The international diabetes federation risk score calculator
(IDFRSC) was chosen for this study for notable reasons (see 2.6 and 2.7 for
details) and the risk factors utilized for this calculator as age (years),
gender (male or female), height (m), weight (kg), body mass index (BMI, kg/m2),
fasting blood glucose (FBG, mm/DL), family history (maternal or paternal line),
physical activity (active or not active), fruits and vegetables intake
(everyday or not everyday), blood pressure; systolic blood pressure and
diastolic blood pressure (SBP/DBP), and
the risk score points.
1.2 THE CLINICAL ALGORITHM FOR DM
According to the American Association of clinical Endocrinologist (AACE)
and American college of Endocrinology (ACE), the clinical algorithm is for
comprehensive management of persons with type 2 diabetes (T2D), developed to
provide clinicians with a step-by-step practical guide that considered the
whole patient, his/her spectra of risks and complications, as well as evidenced
approaches that elicit prevention and treatment (see appendix for details).
The following are the key steps of
the algorithm;
1. Lifestyle modifications helps
good living e.g. weight control, physical activity, sleep etc.
2. Avoid hypoglycaemia.
3. Avoid weight gain and obesity.
4. Individualize all glycaemic
targets eg glycated haemoglobin (AIC), fasting plasma glucose (FPG), postprandial
plasma glucose (PPG).
5. Optimal AIC is <6.5% or as
close to normal as is safe and achievable.
6. Therapy choices are affected by
initial AIC, duration of diabetes and obesity status.
7. Choice of therapy reflects
cardiac, cerebrovascular and renal status.
8. Comorbidities must be managed for
comprehensive care.
9. Clinical goals are gotten as soon
as possible adjustable at 3months or
less until clinical goal is met.
10. Choice of therapy should be easy
to use and affordable.
11. AIC should be 6.5% or less for
those on any insulin regimen as long as continuous glucose monitoring (cgm) is
being used (AACE and ACE, 2019).
1.3 AIM OF THE STUDY
The study is aimed at calculating a
10-year risk of Diabetes mellitus and its associated health consequences in a
selected young normal healthy adult population in Umudike, Abia state, Nigeria
1.4 OBJECTIVES OF THE STUDY
The specific objectives of the study
was to use a diabetic risk calculator and other selected hematologic indices
to;
i.
To determine the proportion of the study population at high risk
of developing diabetes mellitus.
ii.
To determine the portion of the population already diabetic.
iii.
To help those at high risk understand their health status for
early intervention, lifestyle modification and prevention.
1.5 JUSTIFICATION FOR THE STUDY
Diabetes is a wide-spread long-term
disease that kills millions of people in Nigeria and indeed around the world.
Diabetes prevalence in Nigeria stands at a staggering figure of 1.56 million
persons living with the disease. In Africa, a projected 14,835,000 more people
will live with the disease in less than 2 decades (2030). Sao Tome and Principe
is the least-affected in the continent, with just about 1000 people living with
the disease and a double of that figure by 2030. In the United States of
America (USA), more than 2 million people live with the Disease and 1.9 million
new cases of diabetes being diagnosed in the last 20 years, while 7 million
cases were reported undiagnosed in 2010. China poses as the diabetic capital of
the world with her 92 million people affected by the disease and a projected
150 million by 2030. This means 1 in every 10 Chinese has DM. India, the
world’s second affected region is projected to have 109 million people living
with diabetes by 2035. Currently, the incidence rate (risk) of diabetes in the
world shows that 1 in every 30 people is at risk of developing diabetes by the
2050. Going by this incidence trend in diabetes epidemiology around the globe,
DM will become a real global pandemic by 2030. Today, diabetes takes more lives
than HIV/AIDS and breast cancer combined, claiming the life of 1 person every 3
minutes worldwide (National Health Service UK, 2014 and ADA, 2016). The study
will give an insight into the prospective emergence of Diabetes Mellitus within
the next decades by using standard anthropometric protocols and other defined
modern integrative techniques and approach by statistically analysing the
results gotten from the data obtained in this study. This will assist the
evaluation and the possible calculation
of the risk of diabetes mellitus in Nigeria by finding those at high risk for
DM for the coming decades.
1.6 STATEMENT OF PROBLEM
This study seeks to determine the
risk of diabetes in Nigeria over a defined period of time, using the standard
anthropometric indices and Diabetic Risk Calculator, and other selected
biochemical parameters. However, while this study will give a possible
correlation between genetic, environmental, nutritional and medical
susceptibilities with chances of developing diabetes by predicting the
prospects in human years, the data gotten from respondents may not be so
accurate as the responders may not morally respond to methods described above.
Also, there is a tendency for selection bias to be positive as people who
volunteer for assessment studies are responders that are more health conscious
to the rest of the population. This may have a better outcome in diagnosis.
This study is limited to healthy young adults (>18 <30 years) and will
not include the aged (>30years) and
pregnant mothers as such cannot predict the diabetes risk in these group
of people. Furthermore T2D is common at >40years of age and a study
population of this age bracket maybe
ideal for studying Diabetes, but this study investigated and predicted the risk of diabetes and not diabetes itself,
and therefore the ideal age would be to use subjects <40years of age who are
already healthy at present but stand a chance of developing the disease in
future. Also, most biochemical studies use a sample size of 30> or <50
for specificity of results, but for human population studies the sample size is
usually>100 to understand the growing trend or pattern amongst the
controlled population. This study therefore used a large population (>100)
to understand the distribution pattern of the disease in umuahia and Nigeria at
large.
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