THE ROLE OF DIETARY PATTERNS IN GYLCEMIC CONTROL IN YOUTH
WITH TYPE 1 DIABETES IN RWANDA
by
Kathryn Kopania
BA, Hofstra University, 2012
Submitted to the Graduate Faculty of
Department of Epidemiology
Graduate School of Public Health in partial fulfillment
of the requirements for the degree of
Master of Public Health
University of Pittsburgh
p
2015
UNIVERSITY OF PITTSBURGH
GRADUATE SCHOOL OF PUBLIC HEALTH
This essay is submitted
by
Kathryn Kopania
on
April 20, 2015
and approved by
Essay Advisor:
Trevor Orchard, MBBCh, MMedSci, FAHA, FACE
Professor of Epidemiology
Department of Epidemiology
Graduate School of Public Health
University of Pittsburgh
Essay Reader:
Catherine Haggerty, PhD, MPH
Associate Professor of Reproductive Epidemiology
Department of Epidemiology
Graduate School of Public Health
University of Pittsburgh
___________________________
___________________________
Essay Reader:
Ravi Sharma, PhD
___________________________
Assistant Professor
Department of Behavioral and Community Health Sciences
Graduate School of Public Health
University of Pittsburgh
(If you have an extra reader, add their info; you can adjust the spacing on this page to fit it.)
ii
Copyright © by Kathryn Kopania
2015
iii
Trevor Orchard, MBBCh, MMedSci, FAHA, FACE
THE ROLE OF DIETARY PATTERNS IN GYLCEMIC CONTROL IN YOUTH
WITH TYPE 1 DIABETES IN RWANDA
Kathryn Kopania, MPH
University of Pittsburgh, 2015
ABSTRACT
Diabetes is a serious public health issue that is increasingly undermining the health and
well-being of individuals world-wide. The disease particularly burdens low and middle-income
countries, where spending and resources for treatment and prevention are scarce. To address the
inadequacies in diabetes care, the Life for a Child Program (LFAC) has been established for
youth diagnosed with Type 1 Diabetes in many low and middle-income countries, including
Rwanda. The LFAC program works throughout all of Rwanda alongside the Association
Rwandaise des Diabetiques (ARD) to provide specialized care to youth in order for them to
successfully manage their diabetes and prevent complications or even death. However, given that
Rwanda is a low-income country, food insecurity is widespread, which presents a challenge to
youth with type 1 diabetes, as diet is a vital component in diabetes management. Specifically, the
timing, frequency, and content of meals is important as individuals must coordinate their insulin
dosage with food intake to prevent hyperglycemia or hypoglycemia, which will help them
achieve glycemic control. This paper examines the role of the dietary patterns of youth
diagnosed with Type 1 Diabetes in Rwanda to assess whether specific dietary patterns are
associated with better glycemic control.
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TABLE OF CONTENTS
1.0
INTRODUCTION ........................................................................................................ 1
2.0
METHODS ................................................................................................................... 4
3.0
RESULTS ..................................................................................................................... 9
4.0
DISCUSSION ............................................................................................................. 15
5.0
CONCLUSION........................................................................................................... 20
APPENDIX A: OVERVIEW ..................................................................................................... 21
APPENDIX B: TABLES AND FIGURES ................................................................................ 26
BIBLIOGRAPHY ....................................................................................................................... 38
v
LIST OF TABLES
Table 1: Characteristics of the 2014 LFAC Cohort Overall and by Gender ............................... 26
Table 2: Spearman Correlation Coefficients and P-Values for HbA1c and Dietary Variables
Overall and by Gender .................................................................................................................. 28
Table 3: Mean HbA1c for Dietary Variables ................................................................................ 29
vi
LIST OF FIGURES
Figure 1: Meal Frequency Distribution......................................................................................... 30
Figure 2: Percent Distribution of Meal Frequency Categories ..................................................... 31
Figure 3: Percent Distribution of Large Meals ............................................................................. 32
Figure 4: Percent Distribution of Snacks ...................................................................................... 33
Figure 5: Percent Distribution of HbA1c Categories .................................................................... 34
Figure 6: Mean HbA1c by Meal Frequency Categories ............................................................... 35
Figure 7: Mean HbA1c by Large Meal Categories ....................................................................... 36
Figure 8: Mean HbA1c by Snack Intake....................................................................................... 37
vii
1.0
INTRODUCTION
Diabetes is an emerging public health issue in Sub-Saharan Africa. It is estimated that
19.8 million people in the region currently live with diabetes, a number that is expected to
increase 109% to 41.4 million by 2035 (1). The three main types of diabetes are type 1 diabetes,
type 2 diabetes, and gestational diabetes, all of which occur when insulin is not produced or used
effectively (1). Type 1 diabetes is an autoimmune disorder, which presents itself mainly in
children and adolescents and requires life long insulin dependency (1, 2). Type 2 diabetes is the
most common type and is characterized by insulin resistance that results in a build up of glucose
in the blood, typically attributed to lifestyle factors including diet and inactivity (1, 2).
Gestational diabetes occurs during pregnancy, when hormones produced by the placenta block
insulin; this blockage results in insulin resistance and consequently high blood sugars and carries
a high risk of type 2 diabetes later in life (1).
Within the African region, youth with type 1 diabetes often go undiagnosed. However,
even if diagnosed in a timely manner, low and middle-income countries have limited resources
that make it difficult for individuals with diabetes to effectively achieve glycemic control, which
leads to the development of diabetes complications and subsequently death. In particular, food
insecurity, or the insufficient access to safe and nutritional food, is a major risk factor for poor
glycemic control and hypoglycemia(3). Food insecure individuals are forced into poor eating
patterns and have to skip meals, reduce meal size, and sometimes enter binge-fasting cycles (4).
1
As a result, those who are food insecure and have diabetes report a greater number of
hypoglycemia and ketoacidosis events and have higher HbA1c levels (3). Thus, these individuals
face optimal diet barriers, as they are unable to modify their daily food selection to coordinate
with their insulin regimens. This especially occurs because they have a fear of hypoglycemia,
which leads to inadequate insulin dosage and growth issues, as well as poor glycemic control.
Limited financial resources also restrict their ability to purchase quality food and appropriate
glycemic control equipment (5).
Children diagnosed with poorly controlled type 1 diabetes (T1D) often experience weight
loss, dehydration, and insufficient energy intake, and as a result, hydration as well as insulin
initiation are necessary to restore normal weight gain, growth, and development; however, since
energy requirements change with age, frequent nutritional assessments should be established (5).
Diet is a crucial component in controlling blood sugar levels. A balanced diet consisting of fats
and proteins, and avoiding high glycemic index foods is recommended for diabetes management.
Additionally carbohydrate intake is central individuals with type 1 diabetes, who can develop
short-term complications, such as hypoglycemia, hyperglycemia or diabetic ketoacidosis, as a
result of mismatched carbohydrate intake and insulin, as evidence suggests total meal
carbohydrate content is vital in post-prandial glucose response and therefore insulin dosage (4,5).
Therefore, children who have fixed insulin, particularly long acting doses require appropriate
food intake in terms of both content and timing (5). Studies have shown the use of carbohydrate
counting and insulin-to-carbohydrate ratio as sources of diabetes management increase patient
satisfaction and decreased HbA1c levels (5).
The two most widely used insulin regimens for type 1 diabetes are the combination of
neutral protamine Hagedorn (NPH), an intermediate acting insulin lasting twelve hours, and
2
regular insulin (R), a short acting insulin lasting six hours or the basal bolus regimen. The NPH
and R combination can be combined into a single injection and is usually taken in the morning
and evening with meals (6). The combination regimen allows individuals to adjust the dosage of
each type independently according to blood sugars and meal size, time, and content. A premixed
NPH and R insulin regimen is available and intended to simplify dosing but does not allow the
NPH or R to be changed independently for individuals. Thus, premixed insulin regimens suffer
from poor flexibility, which increases the risk of hypoglycemia, as a fixed meal plan is necessary
(7). The basal bolus regimen comprises long acting basil insulin, which lasts approximately 24
hours after a once-daily administration and is supplemented with doses of short acting insulin at
each meal, which closely mimics physiological insulin secretion (7). This regimen allows
individuals to independently adjust the dosages according to blood sugar, and as a result presents
greater flexibility over meal times and a varied dose response to different carbohydrate quantities
in meals (8).
As the effect of diet on glucose control in Rwandan youth with type 1 diabetes has not
been previously studied, the primary objective of this paper is to examine the role of dietary
patterns on glycemic control among type 1 diabetic youth who participated in the Life For a
Child (LFAC) program. Using routinely collected data on dietary habits, including meal
frequency, meal size, and snacks, in addition to clinical indicators such as HbA1c and blood
pressure, this paper seeks to determine whether associations exist between the size of meals,
frequency of meals, and snack intake on HbA1c. This work builds upon that of prior doctoral
students who collaborated on the LFAC study, including that of Sara Marshall who examined
various aspects of diabetes care and management and reported on clinical status, glucose control,
and complication rates in this cohort.
3
2.0
METHODS
This paper involves a project evaluation of the LFAC program in collaboration with the
Association Rwandaise des Diabetiques (ARD) and The University of Pittsburgh Graduate
School of Public Health. The University of Pittsburgh’s IRB has determined that this project is
exempt from review under the ‘Existing Data’ category.
Study Population:
The participants of this program evaluation are registered participants of the
Rwanda Life for A Child Program. In order to be enrolled in the program, the participant
must be a resident of Rwanda, 25 years of age or younger, and in need of assistance
obtaining diabetes supplies. Participants of the LFAC program arrived at the ARD or
district hospital for care or were referred to the ARD by a healthcare provider.
Data Collection:
This project will focus on data that were collected from 256 participants between May
2014 and July 2014. Data were collected by the ARD staff, who were aided by myself, a
University of Pittsburgh Graduate School of Public Health student during this period. As a
student, I received training prior to arriving in Rwanda on how to conduct the relevant clinical
and laboratory assessments.
4
LFAC annual or quarterly forms and protocols were used to collect data at the ARD and
twenty district hospitals, utilizing previously and routinely collected data that is used for clinical
program purposes. No data were collected for research purposes. Medical and clinical
examinations, facilitated by ARD staff, were conducted, as detailed below.
LFAC Examination Forms:
The LFAC program has developed examination data protocols for annual and quarterly
clinical assessments, the former of which is required for each participant supported by the
program.
Complete clinical and complication history of each participant enrolled in the program
was abstracted annually using standardized LFAC forms. Information on these forms include:
date of birth, date of diagnosis, meter status, insulin regimen, number of insulin injections,
number of insulin units per day, blood pressure medication, number of annual clinic visits,
weight, height, blood pressure, neuropathy assessment (as determined by a monofilament and
tuning fork test), vision assessment, HbA1c, Albumin-Creatinine (A/C) ratio, school attendance
(those attending school and if those attending school are in the appropriate grade for age),
number of hyper/hypoglycemic events, and number of hospitalizations.
Quarterly LFAC forms are used every three months to monitor the participants’
adherence to glucose monitoring and administration plans. Information collected on these forms
includes: glucose monitoring per week, insulin regimen, weight, height, blood pressure, HbA1c,
and A/C ratio (only if annual assessment value was >30 mg/g).
5
Laboratory Data:
MPH students and ARD staff collected a blood sample on each participant, by a
finger prick, which was used to collect a blood sugar, using a Nipro TRUETrack
glucometer, and HbA1c, using the Siemens DCA Vantage System. The Nipro
TRUETrack meter reports blood glucose values of 20-600 mg/dl and any values outside
this range are reported as “Lo” or “Hi”. The Siemens DCA Vantage reports a maximum
HbA1c value of >14%, therefore, for data analysis purposes these results were recorded
as 14.1%. A spot urine sample was also collected from each patient and processed using
the Siemens DCA Vantage System to report A/C ratio.
Complication Assessment:
Neuropathy: Neuropathy was assessed using a monofilament and 128 Hz tuning fork test.
The monofilament was applied to the dorsum of each of the big toes of participants a total of ten
times. A response to seven or more of the ten applications on each foot was considered a normal
response. The vibrating tuning fork was applied to the dorsum of each big toe, and an abnormal
test result was recorded if a patient was unable to feel the vibrations within ten seconds. The
presence of neuropathy was recorded if either/or both an abnormal monofilament and abnormal
tuning fork resulted.
Hypertension: Hypertension was defined as having a systolic blood pressure ≥ 130
mmHg or diastolic blood pressure ≥ 80 mmHg in patients 18 years or older. For patients younger
than 18 years of age, hypertension was defined as a systolic and/or diastolic blood pressure ≥ the
95th percentile according to their age, sex and height, using the CDC Growth Charts and
Department of Health and Human Services Blood Pressure Percentile Tables.
6
Microalbuminuria: Microalbuminuria (MA) was defined as an albumin/Creatinine (A/C)
ratio of 30-299 mg/g in a spot urine sample.
Nephropathy: Nephropathy was defined as an A/C ratio of greater or equal to 300 mg/g
in a spot urine sample.
Dietary Patterns:
As part of the clinical assessment, the ARD staff ascertains information on dietary habits,
including the frequency, size, and timing of meals as well as snack intake in order to prescribe
the correct insulin regimen and successfully achieve glucose control.
Meal Frequency: Meal frequency was measured as the number of times a participant ate
per day. However, given the food insecurity in the country, if the number of meals fluctuated
each day the midpoint was taken.
Meal Size: Meal size was determined to be small, medium, or large portion sizes as
demonstrated to participants by the staff using hand gestures. No standard portion size models
were used.
Meal Times: The timing of meals was defined as when patients consumed each of their
meals per day. Meal times were divided into morning, afternoon, and evening.
Snacks: A patient was recorded as taking a snack if they responded positively to
consuming a small amount of food between meals at least once per week.
Meal points: To provide a summary score of food intake a meal point score was
calculated. The size of meals were assigned a numeric value of 1 for small, 2 for medium, and 3
for large. These points were then assigned to each meal per day and the resulting sum was the
total number of meal points per day. If the number of meals per day fluctuated, the midpoint was
taken.
7
Data analysis:
Descriptive statistics, which include mean, standard deviation, median, and frequencies,
were calculated for all variables. Median and interquartile range were reported if the variable
was non-normally distributed and mean and standard deviation were reported if the variable was
normally distributed. Two-sample t-tests were used to assess differences among continuous
variables and chi-square tests were used to assess differences among categorical variables. A pvalue of <0.05 was used to determine whether a significant association existed among variables.
When analyzing data on nutrition, only participants who reported complete data on nutrition
were included. The analysis for this report was generated using SAS software Version 9.4 of the
SAS System for Windows copyright 2014 SAS Institute Inc.
8
3.0
RESULTS
Data were collected on 253 youth between 29 May 2014 and 8 July 2014. The study
population consisted of slightly more females than male, with 60% (n=153) females and 40%
(n=100) males (Table 1). The overall median age was 21 years (Interquartile range 18, 24), age
at diagnosis 17 years (IQR 14, 19), and duration of diabetes 4 years (IQR 2, 6). The median
duration of diabetes for females was 3 years (IQR 2, 6), which was not significantly lower than
the duration of diabetes of 4 years for males (IQR 2, 6), p=0.71
Monitoring and Care:
The vast majority of participants (85%, n= 208) owned a glucometer, which varied little
by sex (males: 82% and females: 86%). Consequently, a total of 82% (n=204) patients, 79% of
males and 84% of females, were able to check their blood glucose 7 or more times per week.
Patients reported a median glucose monitoring per week of 14 (IQR 7, 14), which was the same
for both males and females (table 1).
The median number of insulin injections per day was 2 (IQR 2, 2), with 97% of patients
taking two or more insulin injections per day, with no significant difference by gender (p=0.27).
Overall, the average units of insulin per day were 0.77 ± 0.27 kg per body weight,. Females took
significantly lower mean units of insulin per day 0.73 ± 0.24 kg per body weight compared to
males 0.82 ± 0.31 kg per body weight (p=0.01). Majority of patients, 81% (n=156), were able to
9
routinely visit a clinic for diabetes care at least 12 times per year. Males and females equally
visited a clinic at least 12 times per year (82% and 81%).
Clinical Status:
Ninety-five percent (n=179) of participants were post-pubertal, and this frequency did
not vary significantly by gender, p=0.45 (Table 1). Participants had a median height of 156 cm
(IQR 149.3, 162.5), a median weight of 51 kg (IQR 44, 58), and a median BMI of 20.31 kg/m2
(IQR 18.8, 22.6). Males had a significantly higher median height 161.3 cm (IQR 152.9, 166.5)
than females 154.5 cm (IQR 147.5, 158.6), p=0.0003, and females had higher median weight 51
kg (IQR 44, 58.5) than males 50 kg (IQR 44.5, 57), although it did not differ significantly,
p=0.48. The mean BMI was significantly higher for female patients compared to male patients
(21.5 ± 3.4 kg/m2 vs. 19.4 ± 2.2 kg/m2), p=<0.001.
Few patients, 3.6% (n=7), reported taking prescribed blood pressure medication, and
more females reported using BP medication than males (5.26% vs. 1.25%). Participants
displayed a mean systolic blood pressure of 123.6 ± 16.9 pressure and a mean diastolic blood
pressure of 77.8 ± 11. The mean systolic blood pressure did not differ significantly for males and
females (125.4 ± 16.6 mmHg vs. 122.4 ± 17.1 mmHg), but females had a significantly higher
mean diastolic blood pressure (78.9 ± 11.1 mmHg vs. 76.1 ± 10.7 mmHg), p=0.04. Fifty-four
percent (n=136) of children and youth were hypertensive. Females were more likely than males
to have a blood pressure that fell in the hypertensive range (59% vs. 41%), although it was not
statistically significant, p=0.56.
The overall median A/C ratio was 11.4 (IQR 6.7, 23.6).
Females had an almost
significantly higher median A/C ratio as compared to males 13.6 (IQR 6.8, 25.4) vs. 10.3 (IQR
6.5, 16.5), p=0.057
10
The median HbA1c was 8.8% (IQR 7.3, 11.5), which suggests many patients have
intermediate to poor glycemic control. The median HbA1c was not significantly higher for
males, 9.1 (IQR 7.5, 11.4), compared to females, 8.7 (IQR 7.2, 11.7), p=0.996. Thirty-eight
percent (n=94) of patients recorded an HbA1c of <8%, while 12% (n=29) recorded an HbA1c
>14% (Table 1). Patients with better HbA1c control (HbA1c<8%) were more likely to have a
longer duration (4.3 ± 3.9 vs. 2.7 ± 2.5 vs. 2.7 ± 2.6) than patients with intermediate HbA1c
control (8% <=HbA1c<=14%) and patients with poor HbA1c control (HbA1c>14%). HbA1c
was broken down into categorical variables to differentiate between good control (HbA1c
<7.3%), intermediate control (7.3<=HbA1c<=11.5%), and poor control (HbA1c>11.5%).
Twenty-eight percent (n=71) had good glycemic control, 48% (n=121) had intermediate control,
and 24% (n=61) had poor glycemic control (Figure 5). There was no statistically significant
association between sex and HbA1c category (p=0.6071).
Complications:
While only two patients had either an abnormal tuning fork vibratory sensation or
monofilament response, one male and one female, thirty-one (16%) children and youth had
microalbuminuria. Females had a higher frequency of MA (58% vs. 42%) than males. Those
with MA had younger mean age at diagnosis (15.3 ± 5.1 vs. 16.8 ± 4.3) and longer disease
duration (4.5 ± 4.4 vs. 4 ± 3), than those without MA. The mean HbA1c was higher for those
present with MA (10.7 ± 2.8 vs. 9.2 ± 2.6). Five (3%) participants had overt nephropathy; all of
which were females and two of which had disease duration of 14 years.
Twenty-one (11%) patients reported one or more hospitalizations within the last year.
Females accounted for 57% of reported hospitalization and males accounted for 43%. Four (2%)
patients, all female, reported one or more events of hypoglycemia, which was almost
11
significantly significant, p=0.058. Of those four patients who reported hypoglycemic events, two
monitored their blood glucose more than 7 times per week. Twenty (10%) patients reported one
or more events of ketoacidosis, which largely accounted for the 21 hospitalizations. Furthermore,
among patients who reported one or more events of ketoacidosis, eight had an HbA1c of >14%.
School Attendance:
Among all cohort patients 18 years or younger who are eligible to attend school, 55%
(n=32) were enrolled and attending. Males, who are 18 years are younger, were not significantly
more likely to attend compared to females (73% vs. 44%), p=0.57. Among those patients 18
years or younger attending school, 33% (n=13), expressed their attendance was limited by their
diabetes and 73% were in the appropriate grade level for their age (Table 1). Females 18 years or
younger reported significant school attendance limitations as a result of their diabetes more
frequently than males (45% vs. 18%), p=0.04. Among those 18 or younger, females were no
more likely to report being in the appropriate grade for age compared to males (79% vs. 69%)
p=0.40 (Table 1).
Dietary Patterns:
Overall, the median number of meals consumed by participants was 3 (IQR 2, 3), differed
significantly by gender, as females reported a higher frequency of 3 (IQR 2.5, 3), despite similar
medians compared to males 3 (IQR 2, 3), p=0.01 (Table 1). Majority of patients (62%) in both
sexes consumed 2.5 to 3 meals per day (Table 1 and Figure 1). A significantly higher percentage
of females (67% vs. 54%) consumed 2.5 to 3 meals per day compared to males, p=0.05 (Table 1
and Figure 1). Eighteen (7%) patients consumed one or fewer meals per day, while seventeen
(7%) patients consumed greater than three meals per day (Table 1 and Figure 1). Males
12
consumed one meal or less per day more frequently than females (11% vs. 5%), p=0.05 (Table 1
and Figure 1).
Forty-One percent (n=91) of participants consumed one large meal per day (Table 1 and
Figure 3). The prevalence of participants who consumed no large meals or more than one large
meal were relatively equal (29% vs. 30%) (Table 1 and Figure 3). Males were more likely to
consume one large meal per day (49% vs. 36%) than females, and females were more likely to
consume no large meals (35% vs. 18%) compared to males, p=0.02 (Table 1 and Figure 3).
The overall median number of meal points was 5 (IQR 4.5, 7). (Table 1). The median
meal points did not differ significantly by gender, as males reported a median of 5 (IQR 4, 7) and
females a median of 5 (IQR 5, 7) p=0.33 (Table 1).
More than half (57%) of participants reported taking a snack each day and slightly more
males reported consuming a snack each day as compared to females, although it did not differ
significantly (48% vs. 40%), p=0.23 (Table 1 and Figure 4).
Dietary Patterns and HbA1c:
Overall, patients who consumed three or more meals per day at the lowest average
HbA1c than patients in other meal frequency categories (Figure 6). Males displayed a similar
association between meal frequency category and mean HbA1c, as those who consumed greater
than three meals per day had the lowest mean HbA1c (8 ± 1.6) compared to other meal
frequency groups (10.3 ± 3.1 for one or less meals and 10 ± 2.7 for 1.5 to 2 meals and 9.1 ± 2.3
for 2.5 to 3 meals) (Table 3 and Figure 6). Based on a spearman correlation test, males showed a
non-significant inverse relationship between meal frequency and HbA1c, p=0.09 (Table 2).
Females who consumed greater than three meals per day had a lower mean HbA1c (8.5 ± 3.4)
than females who consumed more than one to two meals (9.6 ± 3) and more than two to three
13
meals per day (9.5 ± 2.7); however, females who consumed one or fewer meals per day had the
same mean HbA1c (8.5 ± 3) as females who consumed three or more meals per day (Table 3 and
Figure 6). Females showed a non-significant inverse relationship in meal frequency and HbA1c,
p=0.46 (Table 2).
The mean HbA1c was lowest for males who consumed two or more large meals per day
(9.1 ± 2.4) compared to males who consumed no large meals (9.3 ± 2.8) and one large meal per
day (9.6 ± 2.4) (Table 3 and Figure 7). There was a non statistically significant positive
relationship between the number of large meals consumed per day and HbA1c among males
HbA1c (p=0.97) (Table 2). Females who consumed no large meals per day had the lowest mean
HbA1c (9.1 ± 2.7) compared to females who consumed one large meal per day (9.2 ± 2.7) and
greater than two large meals per day (10.2 ± 3) (Table 3). There was also a non statistically
significant positive relationship between the number of large meals per day and HbA1c among
females, p=0.10 (Table 2).
Males showed a non-significant inverse relationship between meal points and HbA1c,
p=0.44 (Table 2). Women showed a non-significant positive relationship between meal points
and HbA1c, p=0.52 (Table 2).
Males and females who reported taking a snack both had a lower mean HbA1c than those
not consuming a snack (Table 3 and Figure 8). The mean HbA1c for males consuming a snack
was 9.2 ± 2.3 compared to 9.6 ± 2.6 for males not consuming a snack (Table 3 and Figure 8).
Females consuming a snack also had a lower mean HbA1c (9.3 ± 2.7) than females not
consuming a snack (10 ± 3) (Table 3 and Figure 8). Based on a chi-square test, there was no
statistically significant association between snack intake and HbA1c category (p=0.2383).
14
4.0
DISCUSSION
The youth in this LFAC program had mediocre glycemic control, as median HbA1c was
8.8% (IQR 7.3, 11.5), and 12% (n=29) of patients had an HbA1c greater than 14%. As the
majority of the patients (85%) owned a glucometer, most were able to check their glucose and
82% reported doing so one or more times per week. Glucose control in the LFAC cohort was
much better than neighboring Tanzania, where one study of 99 individuals with type 1 diabetes
reported no patient had a glucometer; consequently, no individual was able to monitor their
glucose levels at home and hospitals were unable to provide sufficient means of glucose
monitoring, and as a result only one participant was able to successfully achieve good glucose
control (9, 35). Additionally, the LFAC youth cohort reported a median of 12 routine clinic
visits, primarily to collect their free insulin, which is distributed monthly and would otherwise be
unaffordable. Similarly, for neighboring Tanzania, a regular supply of insulin is unaffordable,
costing 25% of the minimum wage, and in Malawi one month supply of intermediate acting
insulin cost 19.6 days of wages (35, 36).
Complications related to poor glycemic control were present in a number of children and
youth. The rates of neuropathy, microalbuminuria, and nephropathy were 1%, 16%, and 3%,
respectively. Females were not significantly more likely to present with microalbuminuria and
nephropathy complications compared to males (p=0.77 and p=0.16). The rate of neuropathy in
the LFAC cohort was far less prevalent that other African countries, such as Cameroon and
15
Sudan (9). In a cross sectional study of 300 diabetes patients in Cameroon, the rate of neuropathy
was 27%, however, these patients were older than patients in LFAC cohort, with a mean age of
56.7 ± 12.3 (range 9-92) (38). Similarly, a study conducted in Sudan on 120 type 1 and type 2
diabetes patients found peripheral neuropathy to be present in 66%, however these patients had a
longer duration than LFAC cohort patients of 16.2 ± 7.3 years and 81% of these patients
exhibited poor glycemic control (39). However, rates of microalbuminuria in the LFAC cohort
were similar to rates seen in Tanzania. Among patients in the LFAC cohort, microalbuminuria
was present in 16%, who had a median diabetes duration of four years whereas, in a cross
sectional study of 91 Type 1 diabetes patients in Tanzania microalbuminuria was present in
12.1%, who displayed a median duration of three years (37).
Like most common autoimmune diseases, type 1 diabetes was more prevalent in females
than males in this LFAC population. It is understood that there is a difference in the basic
immune response between men and women, as women are more responsive to vaccinations,
trauma, and infections through an increased production of antibodies (40). Subsequently, a good
predictor of the development of an autoimmune disease is the number of different autoantibodies
present in an individual, and since women produce more antibodies in response to infections they
are at a greater risk of developing autoimmune disorders (40). In Rwanda, the gender difference
is of interest because in most countries there is no gender difference in type 1 diabetes
prevalence. However, this greater female prevalence of type 1 diabetes has also been
documented in other African countries such as Ethiopia, Nigeria, Libya, and Sudan (10). This
follows trends that in regions with low incidence, there is excess in female diabetes cases (11).
Currently, literature discussing the effect of meal frequency and meal size on glycemic
control in individuals with Type 1 Diabetes is scarce. In the LFAC study population, there were
16
statistically significant difference by gender for meal frequency as a continuous variable, meal
frequency as a categorical variables, and number of large meals. The median meal frequency
differed significantly by gender, with females reporting a median of 3 (2.5, 3) and males
reporting a median of 3 (2, 3), p=0.01 (Table 1). Additionally, in both males and females, a
greater number of meals consumed were correlated with a lower HbA1c level, although it was
not statistically significant for either gender (-0.18 p=0.09 for males and -0.62 p=0.46 for
females) (Table 2). While data on meal frequency and HbA1c in youth with diabetes is scarce, a
study of 655 Norwegian youth with type 1 diabetes, researchers concluded that those participants
who skipped meals experienced suboptimal HbA1c, higher LDL cholesterol, and were more
likely to be overweight (41). Additionally, in a study conducted in the greater Washington D.C.
area, of middle aged men and women examined the relationship between reduced meal
frequency on health indicators; researchers concluded participants who consumed one meal per
day had significant increase in hunger, modification of body composition, increases in blood
pressure, total, LDL, and HDL cholesterol concentrations, and significant decrease in
concentrations of cortisol (13).
In the LFAC cohort, the majority of patients (62%) consumed 2.5 to 3 meals per day,
with females significantly eating 2.5 to 3 meals more often than males, p=0.02 (Table 1).
Eighteen (7%) patients consumed one or fewer meals per day, which differed significantly by
gender, as males were reported eating one meal or less per day compared to females p=0.02.
Furthermore, in the LFAC program, 41% (n=91) of participants consumed one large meal per
day, while 29% (n=64) of participants consumed no large meals per day. While research on meal
size and HbA1c in youth with type 1 diabetes is unavailable in the general population, a
crossover design study of 54 type 2 diabetes patients in the Czech Republic that compared eating
17
six small meals to eating two large meals per day, both with the same daily energy restriction,
showed that consuming two larger meals at breakfast and lunch had better effects on hepatic fat
content, fasting plasma glucose, C-peptide, and body weight compared to consuming six small
meals per day (14). These results in the type 2 diabetes patients are consistent the findings in this
report that better HbA1c is associated with meal frequency. Similarly, in a study of twelve type 2
diabetes patients, patients were randomized into either a six small meal group or two large meal
group to assess the effects of meal frequency on blood glucose and serum insulin. Similar to the
results of the Czech Republic study, the latter study concluded that the two large meals group
induced an 84% greater maximum amplitude of glucose excursions and had higher insulin
responses (15).
While the size of meals was recorded in the LFAC cohort, a limitation was data on meal
composition was not recorded. Therefore, it was not possible to assess the amount of
carbohydrates patients were consuming.
However, in a study of data collected on meal
frequency from 1,371 Korean diabetics through the 4th Korea National Health and Nutrition
Examination Survey, mean and women who frequently consumed fish experienced a significant
decrease in HbA1c (p=0.043 and p=0.001) (12). Additionally, women in this study who
frequently consumed legumes experienced a significant decrease in HbA1c, p=0.029. However,
as the consumption of stable carbohydrates increased in patients, so to did HbA1c. As a result,
fish intake in men and women, and legume intake in women, were positively associated with
good glycemic control, while frequent intake of carbohydrates was associated with poor
glycemic control (12).
Although there was no significant difference in snack intake by gender (p=0.23), both
males and females who consumed a snack had lower mean HbA1c levels than those who did not
18
report consuming a snack (Table 3). While many youth may take a snack in the afternoon or
before bedtime to prevent hypoglycemia, this might be an indicator that those who had a better
HbA1c with snack intake are following their regimen more closely than those who did not report
taking a snack.
19
5.0
CONCLUSION
The present data from the LFAC program demonstrate there is the need to conduct
further analysis on the dietary patterns on HbA1c in youth with type 1 diabetes in Rwanda.
Currently, data suggests greater meal frequency is correlated with a decrease in HbA1c in both
males and female youth. However, the greater number of large meals consumed was not
significantly correlated with an increase in HbA1c levels in females but not males, despite
previous research suggesting two large meals better controls a variety of health indicators
compared to six smaller meals, which may result from a high carbohydrate intake given the food
content of the country. Larger, more frequent meals in addition to snack intake in the LFAC
cohort reflected better ability to follow insulin regimen more closely, and thus have better
glycemic control. Therefore, more information on dietary patterns, particularly diet composition,
is necessary to further assess the impact the size of meals on HbA1c levels.
With more
information of dietary habits, the patients, families, and ARD staff will be better suited to make
timely and appropriate adjustments to insulin regimens to achieve better glycemic control.
20
APPENDIX A: OVERVIEW
Rwanda:
Rwanda is a landlocked country located in East Central Africa bordered by Uganda,
Burundi, Tanzania, and the Democratic Republic of the Congo. It is the most densely populated
country in Africa with a population of 12.3 million within 26338 km2 (16). About 90% of the
country’s population is rural and mainly living on subsistence farming products of coffee,
bananas, potatoes, and livestock.
Rwanda is home to three ethnic groups: Hutu, Tutsi, and Twa. Interethnic conflict
between the Hutu and Tutsi gave rise to the country’s 1994 genocide. On April 6, 1994,
Rwandan President Habyarimana’s plane was shot down, which sparked the violence that ensued
over the next three months, during which nearly 800,000 individuals were murdered (17, 18). As
a result of the genocide, Rwanda’s economy, infrastructure, and social development were
destroyed and the ability to attract investment disappeared.
In the years after the genocide Rwanda has made significant, impressive progress, which
has now surpassed the pre-genocide economic levels. Within the past four years, the country has
seen 8% economic growth as a result of agricultural productivity, tourism, and investment in
infrastructure (19). In regards to inflation, growth, and indebtedness, Rwanda has outperformed
21
most countries in the region, and it was named the most attractive African market for businesses
in the African Retail Development Index in March 2014 (19).
In addition to economic growth, Rwanda has experienced positive health trends. The
current health expenditure is 10.8% of the GDP (16). The number of individuals living below the
poverty line fell from 59% in 2001 to 44.9% in 2011, while life expectancy, primary school
enrollment and health care have increased (16,19). The number of non-private health facilities
rose from 541 in 2009 to 720 in 2011, with 1.6 beds per 1,000 and 1 physician per 17,149 (20).
Since 1999, Rwanda has been utilizing Mutuelle de Sante, a community based health insurance
system, which has grown to include 90% of the population and reduced out-of-pocket health
spending from 28% to 12% of the country’s total health expenditure (21). Until recently, the
system’s premiums were $2 a year per person, however the system has now adopted a sliding
scale, as that proved to create financial hardships for many individuals (22). Currently, there are
34 district hospitals and more than 380 health centres that cover the healthcare of the population,
that use basic equipment and store essential medication (17). Although the risk of infectious
diseases remains relatively high, Rwanda is now also challenged with reducing the burden of
chronic diseases.
Diabetes Overview:
Diabetes is a non-communicable disease (NCD) characterized by sustained elevated
blood glucose levels resulting from insufficient insulin production or insulin resistance (1,2,23).
Insulin, a hormone released by beta cells in the Islets of Langerhans of the pancreas, is released
after food is consumed to facilitate the movement of glucose from the blood to the cells for
storage or usage (2,24). Diabetes has expanded its global presence to affect 381.8 million
individuals worldwide, a number that is set to increase 55% to 591.9 million by 2035 (1). An
22
additional 316 million individuals suffer from impaired glucose tolerance, which puts them at
high risk for developing the disease later on in life (1). The disease imposes high human, social,
and economic costs for all countries; however, an alarming 80% of people with diabetes are
living in low and middle-income countries (1). Diabetes complications such as coronary artery
disease, renal failure, and stroke, contribute to the shortened life expectancy and increased
disability and health costs of patients; by 2013, diabetes was responsible for 5.1 million deaths
and $548 billion in healthcare spending (1, 25).
For centuries, infectious diseases such as malaria and more recently HIV have dominated
African healthcare needs. However, as the health of the region begins to shift, NCD’s have
become more prevalent. The prevalence of diabetes in the African region is 4.9%. Although the
African region, compared to other regions (South and Central America, Middle East and North
Africa, etc.), is home to the smallest diabetic population, with 19.8 million people with diabetes,
it is projected that diabetes will increase 109% by 2035 to 41.4 million (1).
Type 1 diabetes (T1D), or insulin dependent diabetes mellitus (IDDM), is an autoimmune
disease in which the body’s immune system destroys the pancreatic islet beta-cells, which results
in the absence of insulin production (23, 25). T1D has an acute, rapid clinical progression that
requires constant exogenous insulin treatment to survive. While not always present, symptoms
include slow healing wounds, frequent urination, extreme thirst, and sudden weight loss (1,2,26).
The causes of this disease are not fully understood, but it is thought that both genetic and
environmental factors play a role in its development. While T1D can affect people of all ages, it
is the most common autoimmune disorder in children and adolescence. An estimated 497,100
children are living with T1D worldwide (1). This number continues to grow, as children under
15 account for 79,000 new cases annually (1). In the African region 39,100 children are living
23
with T1D. However, a large portion of T1D cases go undiagnosed, especially in resource-poor
countries, such as Rwanda, where screening is not a priority, which results in early loss of life.
Even if children are diagnosed in a timely manner, many are unable to afford or obtain the
necessary treatment and monitoring equipment, and as a result succumb to the illness shortly
after diagnosis (1).
Life for A Child:
As communicable diseases persist and non-communicable diseases continue to rise,
developing countries are faced with responding to the double burden of disease. However, due to
scant resources, insufficient data, and limited availability of medical professions, outside support
has been crucial to addressing diabetes care and management with timely, effective programs.
One program in particular is the International Diabetes Federation’s Life For a Child (LFAC)
program, which is supported through Diabetes NSW and Hope worldwide.
Started in 2000, LFAC’s mission is to support the provision of the best possible health
care, given local circumstances, to all young people with diabetes in developing countries
through the strengthening of paediatric diabetes services in these countries (2). To address the
inadequacies of diabetes care, LFAC strengthens services by providing insulin, syringes,
glucometers, diabetes education and training, and HbA1c testing in a few countries. At the end of
2013, there were 13,778 vulnerable youth in 43 countries supported by LFAC (27).
In Rwanda, the LFAC program is well established and operates throughout the entire
country. Here, LFAC aligned with the Association Rwandaise des Diabetiques (ARD) to provide
specialized care to youth with diabetes. Initiated in 2004, with only 25 children, ARD has
steadily grown to support 800 diabetic youth by 2013 (27). Youth obtaining support from the
ARD receive annual clinical exams, which assess diabetes markers to determine management
24
progress. The ARD has been aided by the University of Pittsburgh’s Graduate School of Public
Health, which sends graduate students to help complete the annual assessments.
25
APPENDIX B: TABLES AND FIGURES
Table 1: Characteristics of the 2014 LFAC Cohort Overall and by Gender
Variable
Statistic
Overall
Male
Female
Sex
100%(253)
40%(100)
60(153)
Age (years)
21 (18,24)
21 (18,24)
21 (18,24)
0.43
Diagnosis Age (years)
17 (14,19)
17.1 ± 4.4
17(14,19)
0.10
P-Value
Duration of Diabetes (years)
4 (2,6)
4 (2,6)
3 (2,6)
0.71
Have a Meter %(n) (N=244)
85% (208)
83%(78)
87% (130)
0.43
Glucose Monitoring (per week)
14 (7,14)
14 (7,14)
14 (7,14)
0.27
2 (2,2)
2 (2,2)
2 (2,2)
0.27
Insulin Injections (per day)
Units per day (kg/wt)
0.77 ± 0.27
0.82 ± 0.31
0.73 ± 0.24
0.01
Routine clinic visits (per year)
12 (12,12)
12 (12,12)
12 (12,12)
0.90
Puberty (N=188)
95% (179)
93% (71)
96%(108)
0.45
Height (cm)
156 (149.3,162.5)
161.3
(152.9,166.5)
154.5
(147.5,158.6)
0.0003
Weight (kg)
51 (44,58)
49.6 ± 10.9
51 (44,58.5)
0.48
BMI (kg/m2)
20.3 (18.8,22.6)
19.4 ± 2.2
21.5 ± 3.4
<0.0001
BP systolic (mmHg)
123.6 ± 16.9
125.4 ± 16.6
122.4 ± 17.1
0.16
BP diastolic (mmHg)
77.8 ± 11
76.1 ± 10.7
78.92 ± 11.1
0.04
3.6%(7)
1.3%(1)
5.3%(6)
0.24
BP Meds %(n) (N=194)
26
Table 1 Continued
HbA1c (%)
8.8(7.3,11.5)
9.1(7.5,11.4)
8.7(7.2,11.7)
HbA1c %(n) (N=246)
0.996
0.43
HbA1c <8%
38%(94)
37%(35)
63%(59)
HbA1c >14%
12%(29)
31%(9)
69%(20)
A/C
11.4(6.7,23.6)
10.3(6.5,16.5)
13.6(6.8,25.4)
0.057
Neuropathy %(n) (N=197)
1.0%(2)
1.2%(1)
0.9%(1)
0.49
Hypertension %(n) (N=253)
54%(136)
56%(56)
52%(80)
0.56
Microalbuminuria %(n)
16%(31)
15%(13)
17%(18)
0.77
Nephropathy %(n)
3%(5)
0%(0)
5%(5)
0.16
#Hypoglycemic events %(n) (N=200)
2%(4)
0%(0)
4%(4)
0.058
# Ketoacidosis events %(n) (N=197)
10%(20)
11%(9)
10%(11)
0.88
Variable
Statistic
Overall
Male
Female
P-Value
11%(21)
11%(9)
11%(12)
0.63
31%(62)
33%(28)
30%(34)
0.57
≤18 years of age
55%(32)
73%(16)
44%(16)
>18 years of age
21%(30)
19%(12)
23%(18)
34%(27)
20%(6)
42%(21)
≤18 years of age attending school
33%(13)
18%(3)
45%(10)
>18 years of age attending school
34%(14)
# Hospitalization %(n) (N=195)
Attending School %(n) (N=199)
Attendance limited by diabetes %(n) (N=80)
0.049
23%(3)
39%(11)
70%(38)
76%(19)
66%(19)
≤18 years of age attending school
73%(22)
69%(11)
79%(11)
>18 years of age attending school
67%(16)
89%(8)
53%(8)
Meal frequency (per day)
3 (2,3)
3 (2,3)
3 (2.5,3)
0.01
Meal points
5 (4.5,7)
5 (4,7)
5 (5,7)
0.33
Appropriate grade for age %(n) (N=54)
Snack (per day) (N=222)
0.40
0.23
Yes
57%(126)
52% (44)
60%(82)
No
43%(96)
48% (41)
40%(55)
Large meals (per day) (N=223)
0.02
None
29%(64)
18%(14)
35%(50)
One
41%(91)
49%(39)
36%(52)
Two or More
30%(68)
33%(26)
29%(42)
Meal Frequency Category (N=253)
0.05
<=1
7%(18)
11%(11)
5%(7)
1.5-<=2
25%(62)
30%(30)
21%(32)
2.5-<=3
62%(156)
54%(54)
67%(102)
>3
7%(17)
5%(5)
8%(12)
27
1
Data presented as Mean ± Standard deviation for all normally distributed variables,
median (Interquartile Range) for all non-normally distributed variables or %(n) as appropriate.
Values for two-sample t-tests are presented for continuous variables and either chi-squared or
fisher’s exact are presented for categorical data.
Table 2: Spearman Correlation Coefficients and P-Values for HbA1c and Dietary
Variables Overall and by Gender
Overall:
Variable
Correlation Coefficient
P-Value
Meal Frequency
-0.1
0.13
Total Meal Points
0.04
0.94
Large Meals
0.1
0.14
Snack
-0.1
0.13
Variable
Correlation Coefficient
P-Value
Meal Frequency
-0.18
0.09
Total Meal Points
-0.09
0.44
Large Meals
0.004
0.97
Male:
28
Snack
-0.83
0.45
Variable
Correlation Coefficient
P-Value
Meal Frequency
-0.62
0.46
Total Meal Points
-0.05
0.52
Large Meals
0.14
0.10
Snack
-0.11
0.18
Table 2 Continued
Female:
Table 3: Mean HbA1c for Dietary Variables
No Large Meals
1 Large Meal
2 or More Large P-value
Meals
Male
9.3 ± 2.8
9.6 ± 2.4
9.1 ± 2.4
0.71
Female
9.1 ± 2.7
9.2 ± 2.7
10.2 ± 3
0.16
≤ 1 meal per day
1.5 to ≤ 2 meals
2.5 to ≤3 meals
≥ 3 meals per
per day
per day
day
P-value
Male
10.3 ± 3.1
10 ± 2.7
9.1 ± 2.3
8 ± 1.6
0.15
Female
8.5 ± 3
9.6 ± 3
9.5 ± 2.7
8.5 ± 3.4
0.52
No Snack
Snack
P-value
29
Male
9.2 ± 2.6
9.2 ± 2.3
0.44
Female
10 ± 3
9.3 ± 2.7
0.18
Figure 1: Percent Distribution of Meal Frequency
30
Figure 2: Percent Distribution of Meal Frequency Categories
31
Figure 3: Percent Distribution of Large Meals
32
Figure 4: Percent Distribution of Snacks
33
Figure 5: Percent Distribution of HbA1c Categories
34
Figure 6: Mean HbA1c by Meal Frequency Categories
35
Figure 7: Mean HbA1c by Large Meal Categories
36
Figure 8: Mean HbA1c by Snack Intake
37
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