ABSTRACT
Background
The increase in overweight and obesity cannot be exclusively ascribed to environmental or lifestyle factors, since a substantial proportion of this phenomenon is impacted by genetic and racial factors. This study investigated the link between body mass index (BMI) and fasting blood glucose (FBG) levels among Marma tribal community members in Bangladesh.
Methodology
A cross‐sectional study was carried out on the Marma tribe of the Bandarban using random sampling techniques to select the participants. A total of 208 (74 male and 134 female) respondents participated, and a standardized questionnaire was used to collect data. In addition to descriptive statistics, χ 2, and logistic regression model were employed for analysis at a 5% significance level.
Result
This study reveals that 65.9% of the people belong to the 30–50 age group, followed by 28.8% of the 51–70 age group associated with BMI (p < 0.024). The prevalence of obesity (46%) is higher among females (p < 0.05). Physical activity level is associated with BMI (p < 0.05). Among all the subjects, 14.9% are diabetic, 37.5% are prediabetic, and 46.7% belong to the normal blood glucose range. The relation between physical activity level and body mass index is notable (p < 0.05). Blood glucose level and dietary diversity score are also significant (p < 0.05).
Conclusion
There is no significant association between BMI and blood glucose levels, but a positive correlation exists. The BMI tends to be greater among females. Future research could compare it with non‐tribal groups and use the HbA1c approach for a more comprehensive evaluation.
Keywords: Bangladesh, Blood glucose level, Health status, Marma, Physical activity, Tribal
Abbreviations
- BGL
blood glucose level
- BMI
body mass index
- DDQ
dietary diversity questionnaire
- FAO
Food and Agriculture Organization
- FBG
fasting blood glucose
- GI
glycemic index
- GL
glycemic load
- GPAQ
global physical activity questionnaire
- IFG
impaired fasting glucose
- MET
metabolic equivalent tasks
- PAL
physical activity level
- WHO
World Health Organization
1. Introduction
Overweight and obesity are globally recognized as significant public health risk factors due to their association with health issues like hypertension, insulin sensitivity, diabetes, cardiovascular disease, and various types of cancer in adults [1]. The prevalence of inadequate physical activity is very high among the adult population in Bangladesh [2]. Individuals with higher BMI face an elevated likelihood of succumbing to several ailments, including cancer, cardiovascular disorders, and respiratory complications [3].
According to research published by the World Health Organization in 2016, the prevalence of diabetes in Bangladesh affected 8% of the total population, which amounted to around 12.88 million individuals. Furthermore, diabetes was shown to be the cause of death in 3% of all fatalities across all age groups [4]. Bangladesh has the tenth‐highest number of diabetics worldwide, after Germany and Egypt, with an estimated 8.4 million people. Globally, a projected 15 million people will be diabetic by 2045, up from 14.4 million in 2030 [5].
Bangladesh contains a wide range of indigenous communities. Based on the population census 2022, the number of tribal and indigenous residents was recorded at 1,650,160 [6]. Among them, 388,335 people live in the Bandarban district, including 203,350 men and 184,985 women. The urban population was 100,423 (25.86%) and the rural population was 287,912 (74.14%). According to the 2022 census, Bandarban district has a 197,975 ethnic minority population [6]. The existing literature provides little data about the incidence of diabetes and blood sugar levels among the various tribes inhabiting Southeast Asian nations. The Marma, often called Moghs, Mogs, or Maghs, are Bangladesh's second‐largest ethnic group and are concentrated in the Bandarban, Khagrachari, and Rangamati Hill Districts. While some Marmas reside in Tripura, India, and Myanmar, others reside in the coastal Bangladeshi regions of Cox's Bazar and Patuakhali. Approximately 224,261 Marmas reside in Bangladesh, while 35,722 more do so in India [7, 8]. As far as the prevalence of diabetes among the tribal community of Khagrachari in Bangladesh is 6.2% [9]. It has been observed that diabetes and impaired fasting glucose (IFG) exhibit a notably higher prevalence among the indigenous communities residing in the Khagrachari Hill Tracts [10].
Higher body mass index, waist circumference, and overall obesity are seen among both those with and without type 2 diabetes. Additionally, there has been a marked rise in the occurrence of Class III obesity [11]. Several factors, including elevated BMI, diabetes, hyperlipidemia, and hypertension, were shown to be independently correlated with a heightened risk of mortality in individuals diagnosed with severe obesity [12]. Several studies were conducted where BMI and BGL showed significant and insignificant results as well [13, 14, 15, 16, 17]. Dietary pattern plays a crucial role in protecting against non‐cardiovascular diseases, especially different metabolic syndromes and cardiovascular diseases [18, 19]. Obesity and hypertension of the tribal population are associated with the dietary patterns they followed [20].
Data on blood sugar levels and their relation with BMI, particularly among tribal people of Bangladesh, and their relationship to blood glucose levels are insufficient. As far as we are aware, no study has linked the blood sugar level and its correlation with BMI using a glucometer in the tribal community, specifically in Bangladesh. Despite little to no research in this area, much more has to be investigated as the ratio of diabetic patients among the tribal population was not less enough. So, this study aims to find out the relation between BGL and BMI, along with the associated factors, including dietary diversity, that may influence their relationship.
2. Methodology
2.1. Study Setting and Population
The study was conducted in the Marma tribal community of Bandarban. The sample size was calculated using OpenEpi software for a cross‐sectional study [21] with a 6.2% frequency as the prevalence of diabetes among the tribal population [9]. This included a sample of 208 participants aged 30–90 years. The data collection process used in this study was a random sampling method.
2.2. Inclusion Criteria
Both males and females of the Marma tribes who were residing on the hill tracks were included and subjects fell between the 30–90 age group as the prevalence rates of diabetes indicate a consistent trend, starting at 1% between the ages of 30 and 40, and gradually increasing to over 15% for individuals above the age of 80.
2.3. Exclusion Criteria
Marma individuals residing in the city were excluded due to their changed lifestyles. The population who are from other tribes was excluded as well. Pregnant, severely ill individuals were not included in the study. If the fasting period was less than 10 h and the subject didn't give consent to the blood sample, the subject is removed from the data set as well.
2.4. Height and Weight for BMI
Participants' body weight was measured in kilos using a weighing scale, adhering to minimal clothing and no footwear. Height measurements were taken from toes to heads using meters, with participants maintaining an upright posture. BMI was determined by employing the BMI formula, which is expressed as BMI = kg/m² [22, 23].
2.5. Blood Glucose Level
Participants were instructed to observe a 10‐h fasting period to measure FBG levels. A BIONIME RIGHTEST GM700sb glucometer was used to quantify glucose concentration in a fingertip blood sample. A disposable strip was used for testing, with the participant's finger sterilized and blood droplets applied to the strip before the procedure [24]. To get the average value, three blood samples were collected for each participant.
2.6. Physical Activity Level
Physical activity level was measured using the Global Physical Activity Questionnaire (GPAQ). An individual is classified as physically active if they attain a minimum of 600 metabolic equivalent tasks (MET) minutes each week, whereas those who fall below this threshold are deemed inactive [25].
2.7. Dietary Diversity Score
The Dietary Diversity Questionnaire (DDQ), validated by the Food and Agriculture Organization of the United Nations (FAO), was used to measure dietary diversity in a tribal community context. The tool included local foods and language for meal times, following FAO guidelines [26]. Participants recalled food and beverages consumed in the past 24 h, including snacks, sugar, salt, and composite dish components, if dietary intake deviated due to events, celebrations, or illness.
2.8. Ethical Statement
Before the review of the structure, this study was authorized by the Ethical Committee of Daffodil International University, and ethical approval was acquired from the Public Health Department (Ref. No.: FAHSREC/DIU/2023/1115). A written consent form was taken from each participant, and the pros and cons of the present study were also discussed before data collection.
2.9. Data Analysis
The study used SPSS (version 27.0) for data analysis, providing summary statistics for categorical variables. Pearson's χ 2 and correlation tests were used to show relationships among the data. Multinomial regression models were used to estimate odds ratios and 95% confidence intervals. Demographic categories were referenced against categorical outcomes like BMI, BGL, PAL, and DDS. Bivariate models were used to evaluate correlations between BMI and gender with BGL.
3. Results
Table 1 represents the overall frequency and percentage of the demographic status of the respondents. The majority of the cases are from the 30–50 age group, and 64.4% of them are female respondents. Most of the respondents have secondary education (44.2%) as their educational qualification, and 92.8% of them are married.
Table 1.
Distributions of respondents according to their demographic profile.
| Variables | Frequency (N = 208) | Percent (%) | |
|---|---|---|---|
| Age group | < 40 | 63 | 30.3 |
| 40–59 | 98 | 47.1 | |
| 60+ | 47 | 22.6 | |
| Gender | Male | 74 | 35.6 |
| Female | 134 | 64.4 | |
| Education qualification | Illiterate | 16 | 7.7 |
| Primary education | 47 | 22.6 | |
| Secondary education | 92 | 44.2 | |
| Higher secondary education | 40 | 19.2 | |
| Graduation or more | 13 | 6.3 | |
| Employment status | Unemployed | 110 | 52.9 |
| Employed | 98 | 47.1 | |
| Marital status | Married | 193 | 92.8 |
| Unmarried | 4 | 1.9 | |
| Divorced | 11 | 5.3 | |
| Family member | Mean ± SD | 4.80 ± 1.01 | |
| Household income | < 20,000 | 109 | 52.4 |
| 21,000–30,000 | 68 | 32.7 | |
| 31,000–40,000 | 24 | 11.5 | |
| 41,000–50,000 | 3 | 1.4 | |
| > 50,000 | 4 | 1.9 | |
Table 2 demonstrates the correlation between the age group and gender in the variables. The table demonstrates a statistically significant association between gender (p < 0.05) and BMI. Among males, 14.9% are normal and 9.1% are categorized as obese. Among the female population, the incidence of obesity is found to be greater at 27.9%. Significant correlation is observed between age group (p < 0.01) and gender (p < 0.01) with PAL. Physical activity is found more in the 30–50 age group compared to other age groups. 39.9% are moderately physically active, whereas 26.0% are highly physically active in the age group. Females are more physically active in the moderate physical activity category (49.0%) compared to their male counterparts. A significant result is also observed between BGL and the age group (p < 0.05).
Table 2.
Correlation of BMI, PAL, DDS, and BGL with age and gender.
| Variables | N (%) | Age | p value | Gender | p value | ||||
|---|---|---|---|---|---|---|---|---|---|
| < 40 | 40–59 | 60+ | Male | Female | |||||
| BMI | Underweight | 11 (5.3%) | 0.5% | 3.4% | 1.4% | p > 0.05 | 3.8% | 1.4% | p < 0.01 |
| Normal | 77 (37%) | 12.5% | 15.9% | 8.7% | 14.9% | 22.1% | |||
| Overweight | 43 (20.7%) | 6.7% | 9.6% | 4.3% | 7.7% | 13.0% | |||
| Obese | 77 (37%) | 10.6% | 18.3% | 8.2% | 9.1% | 27.9% | |||
| PAL | Low | 7 (3.4%) | 0.0% | 0.5% | 2.9% | p < 0.01 | 0.5% | 2.9% | p < 0.01 |
| Moderate | 126 (60.6%) | 19.7% | 26.9% | 13.9% | 11.5% | 49.0% | |||
| High | 75 (36.1%) | 10.6% | 19.7% | 5.8% | 23.6% | 12.5% | |||
| DDS | Medium | 17 (8.2%) | 2.4% | 3.4% | 2.4% | p > 0.05 | 3.4% | 4.8% | p > 0.05 |
| High | 191 (91.8%) | 27.9% | 43.8% | 20.2% | 32.2% | 59.6% | |||
| BGL | Normal | 99 (47.6%) | 13.9% | 23.1% | 10.6% | p < 0.01 | 17.3% | 30.3% | p > 0.05 |
| Prediabetic | 78 (37.5%) | 13.0% | 17.8% | 6.7% | 13.5% | 24.0% | |||
| Diabetic | 31 (14.9%) | 3.4% | 6.3% | 5.3% | 4.8% | 10.1% | |||
Abbreviations: BGL, blood glucose level; BMI, body mass index; DDS, dietary diversity score; PAL, physical activity level. Bold p values are significant.
The presented data in Table 3 provide evidence of a statistically significant correlation between DDS and BGL (p < 0.05). 49.2% of normal and 37.7% of prediabetic individuals have a high variety of food groups. There is no statistically significant association seen between PAL and BMI with BGL (p > 0.05).
Table 3.
Association of BMI, DDS, and PAL with BGL.
| BGL | p value | ||||
|---|---|---|---|---|---|
| Normal (%) | Prediabetic (%) | Diabetic (%) | |||
| BMI | Underweight | 8 (72.7%) | 2 (18.2%) | 1 (9.1%) | p > 0.05 |
| Normal | 36 (46.8%) | 26 (33.8%) | 15 (19.5%) | ||
| Overweight | 23 (53.5%) | 17 (39.5%) | 3 (7.0%) | ||
| Obese | 32 (41.6%) | 33 (42.9%) | 12 (15.6%) | ||
| DDS | Medium | 5 (29.4%) | 6 (35.3%) | 6 (35.3%) | p < 0.05 |
| High | 94 (49.2%) | 72 (37.7%) | 25 (13.1%) | ||
| PAL | Low | 4 (57.1%) | 0 (0.0%) | 3 (42.9%) | p > 0.05 |
| Moderate | 58 (46.0%) | 50 (39.7%) | 18 (14.3%) | ||
| High | 37 (49.3%) | 28 (37.3%) | 10 (13.3%) | ||
Abbreviations: BGL, blood glucose level; BMI, body mass index; DDS, dietary diversity score; PAL, physical activity level. Bold p value is significant.
A statistically significant association is observed between BMI and PAL (p < 0.05) (Table 4). Among individuals who are moderately physically active, 36.5% of respondents are within the normal BMI range, and 42.1% are obese. 36.0% of respondents belonging to the normal BMI category have high PAL. No correlation is revealed between DDS and BMI (p > 0.05).
Table 4.
Association of PAL and DDS with BMI.
| BMI | p value | |||||
|---|---|---|---|---|---|---|
| Underweight (%) | Normal (%) | Overweight (%) | Obese (%) | |||
| PAL | Low | 2 (28.6%) | 4 (57.1%) | 0 (0.0%) | 1 (14.3%) | p < 0.05 |
| Moderate | 3 (2.4%) | 46 (36.5%) | 24 (19.0%) | 53 (42.1%) | ||
| High | 6 (8.0%) | 27 (36.0%) | 19 (25.3%) | 23 (30.7%) | ||
| DDS | Medium | 2 (11.8%) | 4 (23.5%) | 5 (29.4%) | 6 (35.3%) | p > 0.05 |
| High | 9 (4.7%) | 73 (38.2%) | 38 (19.9%) | 71 (37.2%) | ||
Abbreviation: BMI, body mass index; DDS, dietary diversity score; PAL, physical activity level. Bold p value is significant.
The multinomial logistic regression (Table 5) indicates that diabetic respondents are five times more likely to have medium DDS compared to normal BGL respondents (OR: 5.13, 1.38–19.07, and p < 0.05). There is no significant association found among DDS, BMI, and PAL (Table 6).
Table 5.
Regression summary among BMI, DDS, and PAL with BGL.
| BMI | AOR | Lower bound | Upper bound | ||
|---|---|---|---|---|---|
| BGL | Prediabetic | Underweight | 0.257 | 0.048 | 1.384 |
| Normal | 0.654 | 0.318 | 1.343 | ||
| Overweight | 0.665 | 0.295 | 1.500 | ||
| Diabetic | Underweight | 0.291 | 0.030 | 2.840 | |
| Normal | 1.083 | 0.428 | 2.739 | ||
| Overweight | 0.366 | 0.090 | 1.480 | ||
| The reference category is Normal. | |||||
| DDS | |||||
| BGL | Prediabetic | Medium | 1.439 | 0.412 | 5.022 |
| Diabetic | Medium | 5.132 * | 1.381 | 19.073 | |
| The reference category is Normal. | |||||
| PAL | |||||
| BGL | Prediabetic | Low | 3.384E‐9 | 3.384E‐9 | 3.384E‐9 |
| Moderate | 1.252 | 0.597 | 2.624 | ||
| Diabetic | Low | 1.425 | 0.177 | 11.455 | |
| Moderate | 0.999 | 0.364 | 2.740 | ||
Note: The reference category is Normal.
AOR, adjusted odds ratio (adjusted by age, gender, education, occupation, and income status.
Abbreviations: BMI, body mass index; BGL, blood glucose level; DDS, dietary diversity score; PAL, physical activity level.
p value is < 0.05. Bold value represents significance.
Table 6.
Regression summary of DDS and PAL with BMI.
| DDS | AOR | Lower bound | Upper bound | ||
|---|---|---|---|---|---|
| BMI | Underweight | Medium | 7.581 | 1.003 | 57.309 |
| Overweight | Medium | 2.382 | 0.583 | 9.730 | |
| Obese | Medium | 1.886 | 0.492 | 7.223 | |
| The reference category is Normal | |||||
| PAL | |||||
| BMI | Underweight | Low | 1.729 | 0.080 | 37.188 |
| Moderate | 0.269 | 0.048 | 1.509 | ||
| Overweight | Low | 1.549E‐9 | 1.549E‐9 | 1.549E‐9 | |
| Moderate | 0.521 | 0.202 | 1.344 | ||
| Obese | Low | 0.094 | 0.007 | 1.180 | |
| Moderate | 0.748 | 0.329 | 1.699 | ||
Note: The reference category is Normal.
AOR, adjusted odds ratio (adjusted by age, gender, education, occupation, and income status.
Abbreviations: BMI, body mass index; DDS, dietary diversity score; PAL, physical activity level.
*p value is < 0.05.
4. Discussion
The central dogma of the present study was to evaluate the association of body mass index and dietary diversity with the blood glucose level of the Marma tribes of Bangladesh. Multiple cross‐sectional and longitudinal investigations have shown obesity as a notable modifiable acquired risk factor in the pathogenesis of type 2 diabetes [27, 28]. Research conducted by Skiros et al. examined a group of male participants in Sweden, where the study found a significant correlation between body mass index (BMI) and type‐2 diabetes mellitus incidence, with prevalence rates ranging from 1.4% to 30.3%, suggesting a 22‐fold increase in diabetes risk [29]. It is expected from the study that there exists a relationship between body mass index and blood glucose levels. Nevertheless, this assertion does not hold in all circumstances. A previous study done in Scotland showed a dearth of statistically meaningful correlation between random blood sugar levels and body mass index (BMI) [30]. However, the research study aimed to examine the potential influence of racial and other biological variables on the observed disparity, with a special emphasis on females of Caucasian and African American ancestry [31].
This study shows a significant association between gender and BMI (p < 0.01). Cases of obesity are higher among females (27.0%) compared to males (9.1%). It was reported in a study that females were higher on the scale of BMI [32]. Age group and gender are also significant with PAL (p < 0.01). In the case of blood glucose level, the age group shows a notable relationship (p < 0.01).
According to the findings of Fretts et al., men showed a greater inclination towards participating in physical activity (an average of 81.3 MET hours per week). In contrast, females demonstrated a lower level of physical activity, with an average of 43.3 MET hours per week [33]. A similar result from the present study is that 63% of the male counterparts are high in physical activity compared to females (21.3%). A 6.6% prevalence of type 2 diabetes and an 8.5% prevalence of impaired fasting glucose in both male and female tribe members with similar susceptibility were reported in previous literature [9]. The findings of this study are also in line with another study reported by Sayeed et al., where the rate of prediabetic (37.5%) is higher than the rate of diabetic (14.9%). This study does not show a statistically significant association between BMI and BGL, but a positive result is observed (r = 0.019, p > 0.05). It was revealed from earlier literature that females residing in these villages had a notably higher body mass index (BMI) in comparison to their male counterparts. In addition, the data shows disparities in traditional Marma tribes due to gender roles. Men are primary earners and physically active, while women are restricted to marital residences. Genetic variations and race or ethnicity differences may also contribute to these disparities. The American Diabetes Association asserts that heredity significantly influences the development and etiology of type 2 diabetes [34]. Furthermore, there were variations in A1C levels among individuals of different races and ethnicities who have poor glucose tolerance, suggesting a significant correlation [35]. Dietary patterns and lifestyle also play a major role in the case of blood glucose levels, rather than all other factors. A study reported that moderate physical activity after a meal, even for 10 min, can help manage postprandial blood glucose levels, particularly during periods of high blood glucose levels [36]. Marma tribes usually start their day with high carbohydrate‐based breakfasts, which were observed during the study. Males work outside, and the majority of the females engage in domestic work. Individuals relax and sleep after midday meals. Similarly, a pattern was observed after evening meals. This proclivity of lifestyle may influence the factors causing sudden blood sugar increases. There is no notable association seen between BGL and PAL (p > 0.05). This is in line with the reported findings, where no significant disparity was seen in the prevalence of diabetes among obese people in their weekly levels of physical activity [37]. The likely explanation may align with the mentioned rationale for the lack of significant association between BGL and BMI. However, the significance of PAL in BMI is noteworthy (p < 0.05). Individuals who reported engaging in any kind of physical exercise had a decreased likelihood of developing diabetes with an odds ratio of 0.67 [33].
A noteworthy correlation is identified between BGL and DDS (p < 0.05). A favorable association between the inclusion of a wide variety of foods in an individual's dietary patterns and increased consumption of dietary fiber, vitamin C, and calcium was reported. An inverse relation between obesity and the aforementioned nutrients was also reported [38]. Consuming a diverse range of foods is usually correlated with an increased consumption of energy [39]. However, instead of relating obesity only to high DDS, it is important to consider the significant impact of glycemic index on blood glucose levels and overall well‐being. In the tribal population, BMI sometimes showed a negative association with dietary patterns [40]. Salmeron et al. conducted two prospective studies that revealed a direct association between dietary glycemic index (GI) and glycemic load (GL), where the elevated risk of developing diabetes was 2.17 [41] and 2.50 [42]. It is important to note that the inflammatory potential of one's diet may vary depending on race or ethnicity, as cultural and racial differences sometimes lead to variations in dietary choices [43]. The study reveals that Marma communities prefer boiled food with traditional “Nappi” spice, and also prefer meals cooked with oils and spices. A study on the migrated population noticed a significant association between elevated glucose levels and BMI [44], revealing that there is a substantial correlation between BGL and BMI due to changes in dietary patterns.
4.1. Strengths and Limitations
The role of weight and physical activity is crucial in preventing diabetes, and reducing weight is related to a lower BMI. The study about blood glucose levels and body mass index in tribal populations is very limited in Bangladesh, which provides the strength of our study. Another strong point of our study is to evaluate the dietary diversity of the Marma population, which adds new findings to fill up the gaps in the literature on tribal populations. This study limits the accessibility to a wide range of villages populated by the Marma tribe because of the long distance of the hill routes. It was also not possible to analyze changes over time. Another limitation of our study is that we are not able to assess blood glucose level using other established biochemical methods such as HbA1C. Our study did not measure the body composition or other climatic effects on the body of the tribe, which may have a significant effect on blood glucose levels or obesity. This study was a cross‐sectional study which didn't able to estimate the causal‐effect relationship between the variables.
5. Conclusion
The present study analyzed the relationship between body mass index and fasting blood glucose levels among Marma tribal community members in Bangladesh. There is no significant association between body mass index and blood glucose levels, but a positive relationship is observed. The body mass index tends to be greater among female individuals. There is also no significant correlation between blood glucose levels and physical activity levels. An association between the dietary diversity score and blood glucose level is observed. Aside from yielding insignificant outcomes, lifestyle adjustments, such as reducing carbohydrate intake, have the potential to mitigate the risk of developing diabetes and obesity. An adaptive approach needs to be taken to improve the quality of life of the tribal population and to understand the variations of body composition and its effect on different types of diseases. Future research could compare it with non‐tribal groups, use the HbA1c approach for a more comprehensive evaluation, and expand the sample size to include a wider range of tribal subgroups. In addition, studies on phenotypic composition and environmental factors that may influence body composition also need to be carried out. This would improve the study's generalizability and allow for a more detailed examination of cultural, nutritional, and lifestyle variations.
Author Contributions
Sadnan Hossain: writing – original draft, conceptualization, investigation. Akibul Islam Chowdhury: writing – review and editing, supervision, methodology, conceptualization. Md Nawal Sarwer: writing – review and editing, methodology. Fouzia Akter: writing – review and editing. Nasima Akter Mukta: conceptualization, methodology, writing – review and editing, supervision.
Transparency Statement
The corresponding author, Nasima Akter Mukta, affirms that this manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned (and, if relevant, registered) have been explained.
Data Availability Statement
The data that support the findings will be available if required from the date of publication.
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Data Availability Statement
The data that support the findings will be available if required from the date of publication.