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# Editorial: Nutrition and sustainable development goal 10: reduced inequalities

Emmanuel Cohen

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Emmanuel Cohen. Editorial: Nutrition and sustainable development goal 10: reduced inequalities. 2025, 10.3389/fnut.2025.1557562. hal-05410322

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# Nutrition and sustainable development goal 10: reduced inequalities, 2nd edition

**Edited by**

Emmanuel Cohen, Norbert Amougou, Nobuo Nishi, Yuxuan Gu and Nur Indrawaty Lipoeto

**Published in**

Frontiers in Nutrition
Frontiers in Sustainable Food Systems

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# Nutrition and sustainable development goal 10: reduced inequalities, 2nd edition

**Topic editors**

Emmanuel Cohen — UMR7206 Eco Anthropologie et Ethnobiologie (EAE), France

Norbert Amougou — UMR7206 Eco Anthropologie et Ethnobiologie (EAE), France

Nobuo Nishi — National institute of Health and Nutrition, Japan

Yuxuan Gu — Nanjing Normal University, China

Nur Indrawaty Lipoeto — Andalas University, Indonesia

**Citation**

Cohen, E., Amougou, N., Nishi, N., Gu, Y., Lipoeto, N. I., eds. (2025). *Nutrition and sustainable development goal 10: reduced inequalities, 2nd edition.*

Lausanne: Frontiers Media SA. doi: 10.3389/978-2-8325-6100-3

**Publisher's note:** In this 2nd edition, the following article has been updated: [Cohen E (2025)]

Editorial: *Nutrition and sustainable development goal 10: reduced inequalities.*

*Front. Nutr.* 12:1557562. doi: 10.3389/fnut.2025.1557562
Table of
contents

05 Editorial: Nutrition and sustainable development goal 10: reduced inequalities

Emmanuel Cohen

07 The association between personal social capital and health-related quality of life among Chinese older people: A cross-sectional study

Dongdong Jiang, Yajie Yan, Han Zhou and Quan Wang

18 Acute malnutrition and its contributing factors among children under-five years in rural kebeles of Shashemene Oromia, Ethiopia

Nigusie Shifera, Aschalew Endale, Degfachew Debela and Tewodros Yosef

28 Dietary patterns and indicators of cardiometabolic risk among rural adolescents: A cross-sectional study at 15-year follow-up of the MINIMat cohort

Mohammad Redwanul Islam, Syed Moshfiqur Rahman, Katarina Selling, Pieta Näsänen-Gilmore, Maria Kippler, Eero Kajantie, Anisur Rahman, Jesmin Pervin and Eva-Charlotte Ekström

41 Undernutrition and associated factors among internally displaced lactating mothers in Sekota camps, northern Ethiopia: A cross-sectional study

Misganaw Asmamaw Mengstie, Mulugeta Dile Worke, Yalemshet Belay, Endeshaw Chekol Abebe, Tadesse Asmamaw Dejenie, Mohammed Abdu Seid and Muluken Teshome Azezew

49 Factors affecting fruit and vegetable consumption and purchase behavior of adults in sub-Saharan Africa: A rapid review

Barbara Stadlmayr, Ursula Trübswasser, Stepha McMullin, Alice Karanja, Maria Wurzinger, Laura Hundscheid, Petra Riefler, Stefanie Lemke, Inge D. Brouwer and Isolde Sommer

79 Dietary patterns derived using principal component analysis and associations with sociodemographic characteristics and overweight and obesity: A cross-sectional analysis of Iranian adults

Sara Ebrahimi, Rebecca M. Leech, Sarah A. McNaughton, Morteza Abdollahi, Anahita Houshiarad and Katherine M. Livingstone

90 School-based intervention impacts availability of vegetables and beverages in participants' homes

Erin A. Hudson, Marissa Burgermaster, Sophia M. Isis, Matthew R. Jeans, Sarvenaz Vandyousefi, Matthew J. Landry, Rebecca Seguin-Fowler, Joya Chandra and Jaimie Davis

99 Exploring disparities in malnutrition among under-five children in Nigeria and potential solutions: a scoping review

Collins John, Bee Koon Poh, Muhammad Yazid Jalaludin, Godpower Michael, Idris Adedeji, Elizabeth Eberechi Oyenusi, Blessing Akor, Nkwoala C. Charles, Vanitha Buthmanaban and Leilani Muhardi
111 A cross-sectional study on the moderating effect of self-efficacy on the relationship between sociodemographic variables and nutrition literacy among older adults in rural areas of North Sichuan

Shasha Liu, Xiaomei Fan, Li Jiang and Tao Liu

120 Prevalence and predictive factors associated with stunting in preschool children in a governorate of Iraq: a community-based cross-sectional study

Huda J. Mhamad, Zana B. Najmadden, Kaihan H. Hama Salih, Dlkhwaz A. Hama, Hiwa O. Abdullah, Karzan M. Hasan, Honar O. Kareem, Bilal A. Mohammed, Fattah H. Fattah, Berun A. Abdalla, Fahmi H. Kakamad and Shvan H. Mohammed

127 A 30-year trend of dairy consumption and its determinants among income groups in Iranian households

Roshanak Roustaee, Hassan Eini-Zinab, Delaram Ghodsi, Elham Mehrparvar Hosseini, Nasrin Omidvar, Hedayat Hosseini, Seyed Omid Hosseini Mousavi and Hamed Rafiee

140 Public food procurement as a tool of sustainable food and nutrition policy—fat products

Katarzyna Magdalena Brukato, Justyna Nowak, Aleksandra Pietrzykowska, Neza Fras, Petra Ožbolt, Oskar Kowalski and Mojca Gabrijelčič Blenkuš

149 Effectiveness of mangrove sword bean food bar addressed to older people of landslide disaster victims

Fatmah Fatmah

159 Investigating the role of women producers in alternative food networks implementing organic farming in Berlin Brandenburg

Faiza Darkhani
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OPEN ACCESS

EDITED AND REVIEWED BY
Mauro Serafini,
University of Teramo, Italy

*CORRESPONDENCE
Emmanuel Cohen
emmcohen@outlook.fr

RECEIVED 08 January 2025
ACCEPTED 16 January 2025
PUBLISHED 29 January 2025

CITATION
Cohen E (2025) Editorial: Nutrition and sustainable development goal 10: reduced inequalities. *Front. Nutr.* 12:1557562.
doi: 10.3389/fnut.2025.1557562

COPYRIGHT
© 2025 Cohen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Editorial: Nutrition and sustainable development goal 10: reduced inequalities

Emmanuel Cohen*

Eco-Anthropology (EA UMR 7206), Centre National de la Recherche Scientifique (CNRS), National Museum of Natural History, Paris, France

KEYWORDS

nutrition, sustainable development, determinants of health, agro-food systems, urbanization

## Editorial on the Research Topic

### Nutrition and sustainable development goal 10: reduced inequalities

The United Nations Sustainable Development Goals (SDGs) are the cornerstone of the 2030 Agenda for Sustainable Development. Nutrition-related challenges occupy an important place within this agenda. Indeed, the increasing prevalence of cardiometabolic diseases in the last decades constitutes the main concern for public health worldwide and it can have a lasting impact on the development of urbanized societies (1). Moreover, the necessity to develop sustainable and resilient agro-food systems to protect the environment has become a priority in all international policies (2). Accordingly, in a globalized world experiencing an overall urban transition, the Nutrition and Sustainable Development Goal (SDG) 10: Reduced Inequalities, aims to provide new theoretical and empirical insights, on nutrition-related issues situated at the crossroads of health and environmental dimensions, to identify at-risk subgroups in different socio-ecological contexts and to suggest alternatives to improve both human and environmental health.

Through the articulation of 14 relevant contributions that include 10 original case studies conducted worldwide, 1 clinical trial, 2 literature reviews, and 1 brief research report, the SDG 10: Reduced Inequalities provided relevant outcomes along with critical analyses and interpretations to illustrate challenges and initiatives around nutrition-related health and environmental issues. Through contributions focusing on the determinants of nutritional health, but also on issues and orientations of agro-food systems at both the national and regional levels, the SDG 10: Reduced Inequalities provides an original overview of factors leading to at-risk dietary patterns and potential socio-political strategies to address such public health issues in a medium- or long-term perspective. This Research Topic has a specific focus on Low- and Middle-income Countries where the urbanization rates are highest (3), as the incidence of obesity-related cardiometabolic diseases (4), while the sustainability of agro-food systems becomes a social priority with increasing soil deterioration (5).

Hence, the first two contributions, by Shifera et al. and Mengstie et al., focused on the determinants of malnutrition in Ethiopia. These works showed that rural and socially excluded populations are more exposed to undernutrition, both for children and mothers. Such trends are in line with the literature, which highlights that countries in the early stages of urban transition present persistently high rates of stunting, wasting and underweight. In continuity with these works, two contributions in Iran, by Ebrahimi et al. and Roustaee et al., showed that along the urbanization process, the consumption of processed energy-dense foods increases in multiple age groups, with a higher risk of obesity-related cardiometabolic diseases. Such a comparative framework sheds light
on how countries in more advanced stages of urban transition, such as Iran, are more exposed to new forms of malnutrition like over nutrition, compared to countries that still maintain a pre-industrial lifestyle in some spaces, such as Ethiopia or Bangladesh where another relevant contribution was realized by Islam et al. Then, Mhamad et al. showed that the prevalence of stunting among preschool children in Iraq is relatively low in some urban areas of the country. The SDG 10 also benefits from a scoping review conducted by John et al. in Nigeria, the largest economy in Africa, which highlights the evidence of an ongoing double burden of malnutrition among children under five years, between persisting undernutrition favoring wasting/stunting and increasing over nutrition favoring overweight/obesity. Meanwhile, in a high income country like China, Jiang et al. described how the social isolation of the elderly reduces health-related quality of life.

This Research Topic presents several contributions on socio-political strategies to improve nutritional health, especially in urbanized areas, with potential positive fallouts on environmental health. For example, in addition to the previous contribution from Nigeria by John et al., which recommends health policy interventions at all levels (individual, household, regional and national), Stadlmayr et al. conducted a scoping review in sub-Saharan Africa highlighting the necessity to improve the food environment to favor greater access to fruits and vegetables, through food safety, sustainable local production, lower prices and food diversity. Another contribution by Hudson et al. showed that school gardening, nutrition education, and cooking programs delivered to elementary children may positively influence the home food environment in the Austin area of Texas (USA). In the continuity of these works, Liu et al. discussed the positive role of self-efficacy in the relationship between age, social isolation and poorer nutritional literacy in rural China, which is associated with a higher number of chronic diseases. Then, in the suburban area of Berlin in Germany, a contribution by Darkhani underlined the positive role of women in the development of alternative food networks to favor environmentally sustainable modes of production, certified food quality, food safety consciousness, and health and nutrition concerns. In addition, Brukalo et al. highlighted how public procurement of fatty products for Polish educational units can be improved to promote children's health. Hence, standardized guidelines are required to promote healthier food choices, encourage sustainable diets, and ultimately improve the overall health and wellbeing of children. Finally, in the context of the current ecological crisis, specifically the increasing number of

natural disasters in Indonesia, a clinical trial conducted by Fatmah demonstrated that Api-api mangrove, an abundant plant species in coastal areas of the country, consumed as sword bean snack bars constitutes a viable and efficient substitute for emergency food provisions, particularly in disaster-stricken communities.

Accordingly, based on these fruitful contributions, the SDG 10 provides new insights to identify at-risk subgroups for nutrition-related diseases along the global urban transition. Children under five years, older adults and socially excluded populations are all subgroups overexposed to stunting, wasting or overweight, depending on the current stage of urban transition in their respective countries. This Research Topic innovatively presents a clear overview of the ongoing nutritional transition associated with the globalized urbanization process. Moreover, the SDG 10 describes multiple local, national and regional alternatives worldwide to address the public health nutritional issues stemming from this urban transition. Alternative food networks, such as innovative agro-food systems where women can play a major role in Germany; school gardening and nutrition education in Texas (USA); or multilevel food intervention programs based on sustainable local production, food safety and food diversity in Nigeria, constitute a small sample from this large landscape of adaptive strategies to allow healthy population and individual trajectories in growing urban areas worldwide.

## Author contributions

EC: Writing – original draft, Writing – review & editing.

## Conflict of interest

The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

## Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

## References

1. Popkin BM, Ng SW. The nutrition transition to a stage of high obesity and noncommunicable disease prevalence dominated by ultra-processed foods is not inevitable. *Obes Rev.* (2022) 23:e13366. doi: 10.1111/obr.13366

2. Michel-Villarreal R, Hingley M, Canavari M, Bregoli I. Sustainability in alternative food networks: a systematic literature review. *Sustainability*. (2019) 11:859. doi: 10.20944/preprints201901.0011.v1

3. Kundu D, Pandey AK. World urbanisation: trends and patterns. In: Kundu D, Sietchiping R, Kinyanjui M, editor. *Developing National Urban Policies*. Singapore: Springer. (2020). p. 13–49.

4. NCD Risk Factor Collaboration. Heterogeneous contributions of change in population distribution of body mass index to change in obesity and underweight. *Elife*. (2021) 10:e60060. doi: 10.7554/eLife.60060

5. Hossain A, Krupnik TJ, Timsina J, Mahboob MG, Chaki AK, Farooq M, et al. Agricultural land degradation: processes and problems undermining future food security. In: Fahad S, Alam M, Saeed M, Adnan M, Hasanuzzaman M, Ullah H, Ali Khan I, editors. *Environment, Climate, Plant and Vegetation Growth*. Cham: Springer International Publishing (2020). p. 17–61.
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**OPEN ACCESS**

**EDITED BY**
Yuxuan Gu,
Nanjing Normal University, China

**REVIEWED BY**
Na Li,
Peking University, China
Lan Bai,
Nanjing Medical University, China

*CORRESPONDENCE
Quan Wang
wangquan73@whu.edu.cn

†These authors have contributed equally to this work and share first authorship

**SPECIALTY SECTION**
This article was submitted to Nutritional Epidemiology, a section of the journal Frontiers in Nutrition

RECEIVED 16 July 2022
ACCEPTED 22 November 2022
PUBLISHED 08 December 2022

**CITATION**
Jiang D, Yan Y, Zhou H and Wang Q (2022) The association between personal social capital and health-related quality of life among Chinese older people: A cross-sectional study. *Front. Nutr.* 9:995729.
doi: 10.3389/fnut.2022.995729

**COPYRIGHT**
© 2022 Jiang, Yan, Zhou and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# The association between personal social capital and health-related quality of life among Chinese older people: A cross-sectional study

Dongdong Jiang¹,²†, Yajie Yan¹†, Han Zhou³ and Quan Wang¹*

¹Global Health Institute, School of Public Health, Wuhan University, Wuhan, China, ²School of Public Health, Zhejiang University, Hangzhou, China, ³Nanjing Lishui District Hospital of Traditional Chinese Medicine, Nanjing, China

**Background:** Lower health-related quality of life (HRQoL) can result in adverse effects on the health of older people. This study aims to explore the relationship between personal social capital (PSC) and HRQoL among Chinese elderly people from rural-and-urban perspective.

**Materials and methods:** 4,802 samples were included from China's health-related quality of life Survey for Older Adults 2018 (CHRQLS-OA 2018). The PSC, including bonding and bridging social capital (BOC and BRC), was measured by the Chinese version of the Personal Social Capital Scale (PSCS-16). The HRQoL was evaluated by the European Five Dimensions Questionnaire (EQ-5D-3L). Linear and Tobit regression models were conducted to examine the relationship between PSC and HRQoL.

**Results:** The BOC and BRC of rural older people were significantly lower than those of urban older people. Pain/discomfort and anxiety/depression were the most significant health problems affecting the older samples. In the five dimensions, the proportion of rural older people with problems was higher than that of urban older people. Among rural older people, BOC was significantly related to self-rated health and EQ-5D utility index (p < 0.05); while BRC was insignificantly associated with self-rated health (p > 0.05) but related to EQ-5D utility index (p < 0.05). Both BOC and BRC were significantly correlated with self-rated health and EQ-5D utility index (p < 0.05) among urban older people.

**Conclusion:** Our study reveals older people's worrying PSC and HRQoL status. The relationship between PSC and HRQoL suggested that more social support and care of intimates should be encouraged to increase the PSC of older people, especially rural older people.

**KEYWORDS**

Chinese elderly people, personal social capital, health-related quality of life, urban-rural distribution, sociocultural aspects of health and wellbeing
# Introduction

Aging has become a major global public health issue, with an estimated 1.5 billion people aged 60 and over worldwide by 2030 (1). As one low-and middle-income country with the largest population globally, the aging process in China is much faster than in many other countries worldwide in terms of growth rate and proportion (2). The Chinese population over 60 years old has been to 264.02 million, accounting for about 18.70% of the total population in 2020 (3). In contrast, the number of people over 60 years old in China is estimated to increase to 420 million by 2035 (4), indicating that China's aging situation is becoming increasingly severe. Besides, due to the deterioration of the physical functions of the older people with ages, most of them may suffer from certain kinds of diseases, especially chronic diseases (5-7), which will directly affect their health-related quality of life (HRQoL).

According to World Health Organization (WHO), HRQoL refers to that individuals' perception of their position in life in the context of the culture and value systems in which they live and concerning their goals, expectations, standards, and concerns (8). HRQoL reflects the multi-dimensions of health, including physiology, psychology, social function, subjective judgment, and life satisfaction (9). Developed countries first researched HRQoL and mainly focused on the population of children (10, 11), women (12, 13), and patients (14, 15). However, they pay more attention to the older people currently because aging has become one of the global public issues (16-18). Most researchers studied HRQoL of the older people on influencing factors, and have proven that demographic factors (e.g., gender, age, marital status, and living areas) (19-22), health-related behaviors (e.g., drinking, smoking) (23, 24), and chronic diseases (25) can affect the HRQoL of the older people. With the development of the economy and the change in social perception, researchers also found that socioeconomic factors such as income, educational levels, and employment were related to the HRQoL of older people (26). In addition, previous studies have also proved that social relationships (e.g., social capital) were associated with individual health (27-29).

Social capital is regarded as the sum of resources and values based on a network of personal and organizational relationships (30). It describes the characteristics of a society that can achieve common goals (31). Considering the difficulty of collecting collective social capital, most studies focus on personal social capital (PSC). PSC can be further distinguished into two dimensions: bonding social capital (BOC) and bridging social capital (BRC) (32). BOC refers to the trust and cooperation between similar members with some social demographic factors (such as age, social status, etc.), while BRC means connections between community residents whose status and power are different (30). Social capital, as a kind of actual or potential resource, many studies have proved that it plays a key role

whether on a personal or collective level (33). To date, current studies have found that collective-level social capital was positive associated with HRQoL (34, 35), but limited research exists on the relationship between PSC and HRQoL of the older people. Due to the tremendous socioeconomic and health disparities between urban and rural areas in China, this study was conducted from the perspective of urban-rural differences. The hypothesis of this study is that the PSC (BOC and BRC) is related to HRQoL positively among rural and urban older people. Moreover, rare studies distinguish between BOC and BRC while elaborating on the association between PSC and HRQoL. Thus, this study aimed to explore the relationship between PSC (BOC and BRC) and HRQoL among Chinese older people. Considering the other developing countries with huge populations, such as India, Brazil, and so on, Chinese experience on the suggestions about the relationship between HRQoL and PSC among older people can offer certain reference.

# Materials and methods

## Design and participants

The date of this study was obtained from China's Health-Related Quality of Life Survey for Older Adults 2018 (CHRQLS-OA 2018) (36). This cross-sectional survey was conducted during the Spring Festival in 2018, and intended to explore the health status of the Chinese older people aged 60 years old and above. We used convenient sampling to collect data and the survey sites including Henan province, Hubei province, Fujian province, Jiangsu province, etc. According to the study design, volunteers met the following inclusion criteria were considered as our target population: (1) individuals aged 60 years old or above, (2) individuals who voluntarily participated in our survey. But not all participants were included. Therefore, the excluded criteria were (1) individuals who could not conduct normal conversation because of aphasia, deafness, or other critical body illnesses, (2) individuals who had severe mental disorders or had been diagnosed with cognitive impairment, (3) individuals who had lost their daily living abilities. The questionnaire included participants' sociodemographic characteristics, personal social capital, behaviors, lifestyles, mental health, HRQoL, coping styles, etc. Overall, we collected 5,638 questionnaires and 5,442 were valid after data quality control, of which 4,807 were offline samples with an effective rate of 85.26%.

Since the purpose of this study was to explore the relationship between the personal social capital of the elderly and HRQoL, respondents with missing values on personal social capital and EQ-5D were excluded. Finally, 4,802 samples of the older people aged 60 years and above were included in the study.
# Measures

## Assessment of personal social capital

The Chinese Version of the Personal Social Capital Scale (PSCS-16) was adopted to measure PSC (37). The PSCS-16 contains 16 questions, composed of two sub-scales: BOC and BRC, both are formed from four sub-items and each sub-item contains two questions. The BOC contains (a) the perceived social network size, (b) the number of trusted social network members, (c) the number of social network members with resources (such as professional work and social influence), and (d) the number of reciprocal social network members; similarly, the BRC contains (a) perceived group size, (b) whether the group represents an individual, (c) resources owned by these groups and (d) the likelihood of getting help from the group on request (38). These response options of 16 questions were assessed using a five points Likert scale (1 = all, 2 = most, 3 = some, 4 = a few, and 5 = none). The average of two related questions' score is the score for this sub-item, with an overall range of 8-40 points. To be consistent with the EQ-5D scores, the PSCS adopted reverse-code statistically. A higher score indicated that participants possessed more personal social capital.

The PSCS-16 has proven reliable and valid in China (19). In this study, Cronbach's alpha of PSCS-16 total scale, BOC and BRC were 0.965, 0.932, and 0.965, and Kaiser-Meyer-Olkin (KMO) were 0.855, 0.919, and 0.953, respectively.

## Assessment of health-related quality of life

Health-related quality of life was measured using the European Five Dimensions Questionnaire (EQ-5D-3L), which consisted of the EQ-5D descriptive system, the European Five Dimensions Questionnaire Visual Analogue Scale (EQ-VAS) and the Utility Index. The EQ-5D descriptive system measured participants' health status in three levels of severity (no problems, moderate problems, and extreme problems) with five dimensions: Mobility (MO), Self-care (SC), Usual activities (UA), Pain/discomfort (PD), Anxiety/depression (AD) (39). The EQ-VAS score was recorded on a scale with anchor points 0 (worst health state) and 100 (best health state), which reflected their knowledge of health (40).

The EQ-5D utility index system refers to converting the combination of problems in the five dimensions of EQ-5D into a total utility score to evaluate the overall quality of life of the sample population. A higher EQ-5D utility index indicated higher levels of HRQoL (41). This study adopted the utility index system developed by Zhuo et al. (42), a model ranging from 0.1702 to 1.0000.

Previous studies have confirmed EQ-5D-3L's reliability and validity in China (25). The Cronbach's alpha of EQ-5D-3L was 0.786, and KMO was 0.788 in the study.

## Basic demographic characteristics

The basic demographic information of this study included participants' sociodemographic characteristics (gender, age,

marital status, residence), socioeconomic status (annual family income per capita, educational levels, employment), number of chronic diseases and healthy behaviors (smoking, drinking, exercise, number of chronic diseases).

# Statistical analysis

Data were analyzed using Statistical Package for the Social Sciences (SPSS) version 22.0 (SPSS Inc., Chicago, IL, USA) and Stata SE 16.0, with a 95% Confidence Interval (CI) and a statistical significance level of 0.05.

Categorical variables were represented by frequencies and proportions, while metric variables were expressed as mean and standard deviation. The chi-square test was used to test whether there was a difference in sociodemographic characteristics between urban and rural areas and univariate analysis of five dimensions in EQ-5D-3L. Differences in each dimension of personal social capital between rural and urban areas, single factor analysis of EQ-VAS and EQ-5D utility index score among samples with different demographic characteristics were carried out using T-test and Analysis of Variance (ANOVA). The association between personal social capital and five dimensions of EQ-5D was examined by multiple linear regression, which included one initial model and four adjusted models. Linear regression and Tobit regression were, respectively used to analyse the relationship between the social capital of the older people and EQ-VAS and EQ-5D utility scores.

# Results

## General sociodemographic characteristics of respondents

As shown in Table 1, this study consisted of 4,802 older adults; all samples were divided into two groups, among whom 59.45% (n = 2,855) were from rural areas and 40.54% (n = 1,893) were from urban areas.

Overall, of the participants, 49.64% were males and 50.36% were females. Nearly half of older people (44.78%) were under 70 years old, while 17.39% were over 80 years old. Most respondents (63.29%) were currently married. The annual family income per capita of less than 15,000 yuan accounted for the majority of the respondents (35.35%). Over half of the participants had received an education (68.13%), and 69.81% were reported without occupations. There were 52.29% of the samples suffered from chronic diseases, and the proportion of both non-smokers and non-drinkers was over 50% (68.15%, 56.70%, respectively). 74.89% of older people do regular exercise.

The following characteristics were found to be significant statistically differences across these two groups: age ($\chi^2$ = 13.106, *p* = 0.011), marital status ($\chi^2$ = 32.834,
<table><caption>TABLE 1 Sociodemographic characteristics of respondents.</caption><thead><tr><th rowspan="2">Variable</th><th rowspan="2">Description</th><th colspan="2">Total (4,802)</th><th colspan="2">Rural (2,855)</th><th colspan="2">Urban (1,893)</th><th rowspan="2">χ<sup>2</sup></th><th rowspan="2">P</th></tr><tr><th>N</th><th>%</th><th>N</th><th>%</th><th>N</th><th>%</th></tr></thead><tbody><tr><td rowspan="2">Gender</td><td>Male</td><td>2,344</td><td>49.64</td><td>1,404</td><td>49.40</td><td>940</td><td>50.00</td><td rowspan="2">0.162</td><td rowspan="2">0.687</td></tr><tr><td>Female</td><td>2,378</td><td>50.36</td><td>1,438</td><td>50.60</td><td>940</td><td>50.00</td></tr><tr><td rowspan="5">Age (years)</td><td>60-64</td><td>1,033</td><td>21.83</td><td>659</td><td>23.15</td><td>374</td><td>19.83</td><td rowspan="5">13.106</td><td rowspan="5">0.001</td></tr><tr><td>65-69</td><td>1,086</td><td>22.95</td><td>669</td><td>23.50</td><td>417</td><td>22.11</td></tr><tr><td>70-74</td><td>1,102</td><td>23.28</td><td>644</td><td>22.62</td><td>458</td><td>24.28</td></tr><tr><td>75-79</td><td>689</td><td>14.56</td><td>386</td><td>13.56</td><td>303</td><td>16.07</td></tr><tr><td>≥80</td><td>823</td><td>17.39</td><td>489</td><td>17.18</td><td>334</td><td>17.71</td></tr><tr><td rowspan="2">Marital status</td><td>Married</td><td>2,995</td><td>63.29</td><td>1,707</td><td>60.02</td><td>1,288</td><td>68.22</td><td rowspan="2">32.834</td><td rowspan="2">&lt;0.001</td></tr><tr><td>Not married*</td><td>1,737</td><td>36.71</td><td>1,137</td><td>39.98</td><td>600</td><td>31.78</td></tr><tr><td rowspan="4">Family annual income per capita (RMB)</td><td>&lt;15,000</td><td>1,656</td><td>35.35</td><td>1,345</td><td>47.76</td><td>311</td><td>16.64</td><td rowspan="4">1015.495</td><td rowspan="4">&lt;0.001</td></tr><tr><td>15,000-30,000</td><td>1,185</td><td>25.29</td><td>854</td><td>30.33</td><td>331</td><td>17.71</td></tr><tr><td>30,000-45,000</td><td>896</td><td>19.12</td><td>400</td><td>14.20</td><td>496</td><td>26.54</td></tr><tr><td>&gt;45,000</td><td>948</td><td>20.23</td><td>217</td><td>7.71</td><td>731</td><td>39.11</td></tr><tr><td rowspan="3">Educational level</td><td>Illiterate</td><td>1,503</td><td>31.86</td><td>1,269</td><td>44.71</td><td>234</td><td>12.45</td><td rowspan="3">730.309</td><td rowspan="3">&lt;0.001</td></tr><tr><td>Elementary school</td><td>1,603</td><td>33.98</td><td>973</td><td>34.28</td><td>630</td><td>33.53</td></tr><tr><td>Junior high school or above</td><td>1,611</td><td>34.15</td><td>596</td><td>21.01</td><td>1,015</td><td>54.02</td></tr><tr><td rowspan="2">Employment</td><td>Unemployed</td><td>3,297</td><td>69.81</td><td>1,760</td><td>61.80</td><td>1,537</td><td>81.97</td><td rowspan="2">218.360</td><td rowspan="2">&lt;0.001</td></tr><tr><td>Employed</td><td>1,426</td><td>30.19</td><td>1,088</td><td>38.20</td><td>338</td><td>18.03</td></tr><tr><td rowspan="3">Number of chronic diseases</td><td>0</td><td>2,222</td><td>47.71</td><td>1,283</td><td>45.69</td><td>939</td><td>50.78</td><td rowspan="3">14.104</td><td rowspan="3">0.001</td></tr><tr><td>1</td><td>1,260</td><td>27.06</td><td>770</td><td>27.42</td><td>490</td><td>26.50</td></tr><tr><td>≥2</td><td>1,175</td><td>25.23</td><td>755</td><td>26.89</td><td>420</td><td>22.72</td></tr><tr><td rowspan="3">Smoking</td><td>Never smoke</td><td>3,216</td><td>68.15</td><td>1,834</td><td>64.58</td><td>1,382</td><td>73.55</td><td rowspan="3">80.573</td><td rowspan="3">&lt;0.001</td></tr><tr><td>Used to smoke</td><td>407</td><td>8.62</td><td>220</td><td>7.75</td><td>187</td><td>9.95</td></tr><tr><td>Smoking</td><td>1,096</td><td>23.23</td><td>786</td><td>27.68</td><td>310</td><td>16.50</td></tr><tr><td rowspan="3">Drinking</td><td>Never drink</td><td>2,652</td><td>56.70</td><td>1,591</td><td>56.58</td><td>1,061</td><td>56.89</td><td rowspan="3">9.812</td><td rowspan="3">0.007</td></tr><tr><td>Used to drink</td><td>390</td><td>8.34</td><td>208</td><td>7.40</td><td>182</td><td>9.76</td></tr><tr><td>Drinking</td><td>1,635</td><td>34.96</td><td>1,013</td><td>36.02</td><td>622</td><td>33.35</td></tr><tr><td rowspan="2">Regular exercise</td><td>No</td><td>1,179</td><td>25.11</td><td>980</td><td>34.81</td><td>199</td><td>10.58</td><td rowspan="2">352.176</td><td rowspan="2">&lt;0.001</td></tr><tr><td>Yes</td><td>3,517</td><td>74.89</td><td>1,835</td><td>65.19</td><td>1,682</td><td>89.42</td></tr></tbody></table>

p < 0.001), family annual income per capita (χ² = 1015.495, p < 0.001), educational level (χ² = 730.309, p < 0.001), employment (χ² = 218.360, p < 0.001), number of chronic diseases (χ² = 14.104, p = 0.001), smoking (χ² = 80.673, p < 0.001), drinking (χ² = 9.812, p = 0.007), and regular exercise (χ² = 352.176, p < 0.001).

## Scores of personal social capital of the elderly

Table 2 shows the scores of personal social capital among the participants. The respondents' total score of personal social capital was 21.06 ± 7.33, while the score of two dimensions of personal social capital (BOC and BRC) were 11.38 ± 3.62 and 9.67 ± 4.15, respectively. The scores of the BOC and BRC among

older people in rural areas were significantly lower than those in urban areas (p < 0.001).

## Health status distribution on the five dimensions of European Five Dimensions Questionnaire

In this study, pain/discomfort was the most common problem among the older people: 51.52% in rural areas compared with 40.67% in urban areas (p < 0.001). While self-care was the least frequently reported problem: 20.91% in rural areas compared with 13.63% in urban areas (p < 0.001). Five dimensions of EQ-5D-3L were all statistically significant between rural and urban areas (p < 0.001) (Table 3).

<footnote>*Not married includes divorce, separated, widowed, and never married; Sample sizes of the demographic characteristic variables may not sum to *n* = 4802 due to missing values. According to variables sorted in table, the missing data values are 69, 70, 117, 85, 85, 79, 145, 145, 83, 125, and 106, respectively.</footnote>
<table><caption>TABLE 2 Scores of personal social capital among older people (Mean ± SD).</caption><thead><tr><th>Variable</th><th>Total</th><th>Rural areas</th><th>Urban areas</th><th>t</th><th>p</th></tr></thead><tbody><tr><td>BOC</td><td>11.38 ± 3.62</td><td>10.48 ± 3.63</td><td>12.74 ± 3.17</td><td>-22.728</td><td>&lt;0.001</td></tr><tr><td>BRC</td><td>9.67 ± 4.15</td><td>8.49 ± 3.84</td><td>11.46 ± 3.96</td><td>-25.854</td><td>&lt;0.001</td></tr><tr><td>PSC</td><td>21.06 ± 7.33</td><td>18.96 ± 7.02</td><td>24.20 ± 6.63</td><td>-26.085</td><td>&lt;0.001</td></tr></tbody></table>

BOC, bonding social capital; BRC, bridging social capital; and PSC, personal social capital.

<table><caption>TABLE 3 Health status distribution on the five dimensions of European Five Dimensions Questionnaire (EQ-5D-3L).</caption><thead><tr><th rowspan="2">EQ-5D dimensions</th><th rowspan="2"></th><th colspan="2">Rural</th><th colspan="2">Urban</th><th rowspan="2">χ<sup>2</sup></th><th rowspan="2">p</th></tr><tr><th>N</th><th>%</th><th>N</th><th>%</th></tr></thead><tbody><tr><td rowspan="3">Mobility</td><td>No problem</td><td>2,087</td><td>73.10</td><td>1,529</td><td>80.77</td><td rowspan="3">38.496</td><td rowspan="3">&lt;0.001</td></tr><tr><td>Some problems</td><td>739</td><td>25.88</td><td>344</td><td>18.17</td></tr><tr><td>Confined to bed</td><td>29</td><td>1.02</td><td>20</td><td>1.06</td></tr><tr><td rowspan="3">Self-care</td><td>No problems</td><td>2,258</td><td>79.09</td><td>1,635</td><td>86.37</td><td rowspan="3">40.879</td><td rowspan="3">&lt;0.001</td></tr><tr><td>Some problems</td><td>543</td><td>19.02</td><td>235</td><td>12.41</td></tr><tr><td>Unable to</td><td>54</td><td>1.89</td><td>23</td><td>1.22</td></tr><tr><td rowspan="3">Usual activities</td><td>No problem</td><td>2,050</td><td>71.80</td><td>1,494</td><td>78.92</td><td rowspan="3">30.837</td><td rowspan="3">&lt;0.001</td></tr><tr><td>Some problems</td><td>739</td><td>25.88</td><td>362</td><td>19.12</td></tr><tr><td>Unable to</td><td>66</td><td>2.31</td><td>37</td><td>1.95</td></tr><tr><td rowspan="3">Pain/discomfort</td><td>No problems</td><td>1,384</td><td>48.48</td><td>1,123</td><td>59.32</td><td rowspan="3">55.794</td><td rowspan="3">&lt;0.001</td></tr><tr><td>Some problems</td><td>1,393</td><td>48.79</td><td>740</td><td>39.09</td></tr><tr><td>Extreme problems</td><td>78</td><td>2.73</td><td>30</td><td>1.58</td></tr><tr><td rowspan="3">Anxiety/depression</td><td>No problem</td><td>1,844</td><td>64.59</td><td>1,535</td><td>81.09</td><td rowspan="3">153.491</td><td rowspan="3">&lt;0.001</td></tr><tr><td>Some problems</td><td>960</td><td>33.63</td><td>331</td><td>17.49</td></tr><tr><td>Extreme problems</td><td>51</td><td>1.79</td><td>27</td><td>1.43</td></tr></tbody></table>

## Distribution of VAS scores and utility index among older people

Table 4 shows the scores of the samples' self-rated health and utility index. The following characteristics were significantly different among the rural participants in both VAS and utility index scores: gender, age, marital status, annual family income per capita, educational attainment, employment, number of chronic diseases, drinking, and regular exercise (p < 0.05). Significant differences were found in VAS scores in urban samples in age, marital status, annual family income per capita, educational level, employment, number of chronic diseases, smoking, drinking, and regular exercise (p < 0.05). While in utility index scores only age, marital status, annual family income per capita, employment, number of chronic diseases, and regular exercise were found to be significantly different among urban samples (p < 0.05).

## The relationship between personal social capital and European Five Dimensions Questionnaire Visual Analogue Scale

As shown in Table 5, for the rural sample, in model 1, only BOC was positively correlated with the EQ-VAS score

of the older people (B = 0.977, 95% CI = 0.75–1.21). After adjusting for sociodemographic characteristics, socioeconomic status, number of chronic diseases, and healthy behaviors, in model 5, BOC was still positively related to the EQ-VAS score of the older people (B = 0.567, 95% CI = 0.32–0.81), and BRC had nothing to do with the EQ-VAS score of the elderly (p > 0.05).

For the urban sample, in model 1, both the BOC (B = 0.752, 95% CI = 0.47–1.03) and the BRC (B = 0.697, 95% CI = 0.47–0.92) were related to the EQ-VAS score of the elderly positively. After adjusting for sociodemographic characteristics, socioeconomic status, number of chronic diseases, and healthy behaviors, in model 5, both the BOC (B = 0.614, 95% CI = 0.32–0.91) and the BRC (B = 0.349, 95% CI = 0.12–0.58) were still positively correlated with the EQ-VAS score of the participants.

## The relationship between personal social capital and EQ-5D utility index

As shown in Table 6, for the rural sample: in Model 1, the BOC was positively correlated with the utility score of the older people (β = 0.0111, 95% CI = 0.0089–0.0134), while the BRC was negatively correlated with the utility score of the older people (β = -0.0039, 95% CI = -0.0062–-0.0017). After adjusting for sociodemographic characteristics, socioeconomic status, number of chronic diseases, and healthy behaviors, in
<table><caption>TABLE 4 Distribution of VAS scores and utility index among older people (Mean ± SD).</caption><thead><tr><th rowspan="2">Variables</th><th rowspan="2"></th><th colspan="2">Rural areas</th><th colspan="2">Urban areas</th></tr><tr><th>VAS</th><th>Utility</th><th>VAS</th><th>Utility</th></tr></thead><tbody><tr><td rowspan="4">Gender</td><td>Male</td><td>75.14 ± 14.39</td><td>0.929 ± 0.099</td><td>80.63 ± 13.77</td><td>0.953 ± 0.088</td></tr><tr><td>Female</td><td>72.97 ± 15.69</td><td>0.917 ± 0.107</td><td>77.19 ± 14.91</td><td>0.938 ± 0.103</td></tr><tr><td>T</td><td>3.845</td><td>2.826</td><td>5.203</td><td>3.355</td></tr><tr><td>P</td><td>&lt;0.001</td><td>0.005</td><td>&lt;0.001</td><td>0.001</td></tr><tr><td rowspan="5">Age (years)</td><td>60-64</td><td>75.49 ± 15.02</td><td>0.947 ± 0.081</td><td>81.13 ± 14.66</td><td>0.963 ± 0.067</td></tr><tr><td>65-69</td><td>75.92 ± 14.84</td><td>0.939 ± 0.091</td><td>79.21 ± 14.60</td><td>0.949 ± 0.101</td></tr><tr><td>70-74</td><td>74.51 ± 13.81</td><td>0.922 ± 0.105</td><td>78.48 ± 13.92</td><td>0.947 ± 0.101</td></tr><tr><td>75-79</td><td>73.21 ± 13.89</td><td>0.915 ± 0.102</td><td>78.75 ± 13.12</td><td>0.945 ± 0.090</td></tr><tr><td>≥80</td><td>69.46 ± 17.10</td><td>0.873 ± 0.129</td><td>76.93 ± 15.30</td><td>0.923 ± 0.105</td></tr><tr><td rowspan="4">Marital status</td><td>F</td><td>16.122</td><td>43.330</td><td>3.931</td><td>7.820</td></tr><tr><td>P</td><td>&lt;0.001</td><td>&lt;0.001</td><td>0.003</td><td>&lt;0.001</td></tr><tr><td>Married</td><td>76.32 ± 14.36</td><td>0.936 ± 0.099</td><td>80.79 ± 13.66</td><td>0.955 ± 0.091</td></tr><tr><td>Not married</td><td>70.73 ± 15.52</td><td>0.903 ± 0.108</td><td>74.91 ± 15.25</td><td>0.925 ± 0.103</td></tr><tr><td rowspan="4">Family annual income per capita (RMB)</td><td>T</td><td>9.869</td><td>8.109</td><td>8.046</td><td>5.908</td></tr><tr><td>P</td><td>&lt;0.001</td><td>&lt;0.001</td><td>&lt;0.001</td><td>&lt;0.001</td></tr><tr><td>&lt;1,500</td><td>71.51 ± 17.00</td><td>0.907 ± 0.115</td><td>73.54 ± 17.63</td><td>0.916 ± 0.132</td></tr><tr><td>1,500-3,000</td><td>74.12 ± 12.74</td><td>0.924 ± 0.086</td><td>75.46 ± 15.28</td><td>0.931 ± 0.101</td></tr><tr><td rowspan="5">Educational level</td><td>3,000-4,500</td><td>78.26 ± 10.66</td><td>0.951 ± 0.094</td><td>79.78 ± 12.71</td><td>0.952 ± 0.085</td></tr><tr><td>&gt;4,500</td><td>81.43 ± 13.75</td><td>0.960 ± 0.090</td><td>82.41 ± 12.35</td><td>0.961 ± 0.071</td></tr><tr><td>F</td><td>42.124</td><td>29.730</td><td>37.834</td><td>21.508</td></tr><tr><td>P</td><td>&lt;0.001</td><td>&lt;0.001</td><td>&lt;0.001</td><td>&lt;0.001</td></tr><tr><td>Illiterate</td><td>71.57 ± 14.11</td><td>0.909 ± 0.107</td><td>75.59 ± 14.41</td><td>0.935 ± 0.082</td></tr><tr><td rowspan="5">Employment</td><td>Elementary school</td><td>74.87 ± 15.93</td><td>0.929 ± 0.098</td><td>79.82 ± 14.11</td><td>0.945 ± 0.109</td></tr><tr><td>Junior high school and above</td><td>77.86 ± 14.73</td><td>0.942 ± 0.102</td><td>79.26 ± 14.48</td><td>0.948 ± 0.090</td></tr><tr><td>F</td><td>38.622</td><td>22.655</td><td>7.800</td><td>1.751</td></tr><tr><td>P</td><td>&lt;0.001</td><td>&lt;0.001</td><td>&lt;0.001</td><td>0.174</td></tr><tr><td>Unemployed</td><td>72.74 ± 16.21</td><td>0.911 ± 0.112</td><td>78.68 ± 14.69</td><td>0.943 ± 0.100</td></tr><tr><td rowspan="5">Number of chronic diseases</td><td>Employed</td><td>76.11 ± 12.82</td><td>0.943 ± 0.084</td><td>80.03 ± 13.41</td><td>0.956 ± 0.076</td></tr><tr><td>T</td><td>-6.142</td><td>-8.795</td><td>-1.646</td><td>-2.577</td></tr><tr><td>P</td><td>&lt;0.001</td><td>&lt;0.001</td><td>&lt;0.001</td><td>0.010</td></tr><tr><td>0</td><td>77.42 ± 14.57</td><td>0.945 ± 0.093</td><td>82.53 ± 12.57</td><td>0.962 ± 0.106</td></tr><tr><td>1</td><td>73.69 ± 14.34</td><td>0.924 ± 0.096</td><td>77.27 ± 14.86</td><td>0.940 ± 0.106</td></tr><tr><td rowspan="5">Smoking</td><td>≥2</td><td>68.46 ± 15.31</td><td>0.882 ± 0.117</td><td>72.78 ± 15.64</td><td>0.915 ± 0.103</td></tr><tr><td>F</td><td>88.534</td><td>91.827</td><td>75.638</td><td>38.199</td></tr><tr><td>P</td><td>&lt;0.001</td><td>&lt;0.001</td><td>&lt;0.001</td><td>&lt;0.001</td></tr><tr><td>Never smoke</td><td>74.21 ± 14.99</td><td>0.925 ± 0.104</td><td>79.44 ± 14.35</td><td>0.947 ± 0.097</td></tr><tr><td>Used to smoke</td><td>74.54 ± 15.37</td><td>0.921 ± 0.100</td><td>78.37 ± 15.04</td><td>0.951 ± 0.099</td></tr><tr><td rowspan="5">Drinking</td><td>Smoking</td><td>73.40 ± 15.27</td><td>0.922 ± 0.104</td><td>77.19 ± 14.27</td><td>0.935 ± 0.090</td></tr><tr><td>F</td><td>0.927</td><td>1.636</td><td>3.243</td><td>2.349</td></tr><tr><td>P</td><td>0.396</td><td>0.195</td><td>0.039</td><td>0.096</td></tr><tr><td>Never drink</td><td>73.63 ± 15.12</td><td>0.921 ± 0.106</td><td>77.45 ± 15.02</td><td>0.942 ± 0.095</td></tr><tr><td>Used to drink</td><td>71.23 ± 14.81</td><td>0.897 ± 0.125</td><td>81.53 ± 11.82</td><td>0.955 ± 0.061</td></tr><tr><td rowspan="5">Regular exercise</td><td>Drinking</td><td>75.23 ± 14.81</td><td>0.929 ± 0.095</td><td>80.81 ± 13.94</td><td>0.950 ± 0.103</td></tr><tr><td>F</td><td>7.412</td><td>8.599</td><td>13.926</td><td>2.495</td></tr><tr><td>P</td><td>0.001</td><td>&lt;0.001</td><td>&lt;0.001</td><td>0.083</td></tr><tr><td>No</td><td>69.92 ± 13.84</td><td>0.906 ± 0.102</td><td>68.37 ± 17.64</td><td>0.871 ± 0.155</td></tr><tr><td>Yes</td><td>76.18 ± 15.26</td><td>0.931 ± 0.104</td><td>80.21 ± 13.42</td><td>0.955 ± 0.082</td></tr><tr><td rowspan="5">Regular exercise</td><td>T</td><td>-11.020</td><td>-5.975</td><td>-9.164</td><td>-7.448</td></tr><tr><td>P</td><td>&lt;0.001</td><td>&lt;0.001</td><td>&lt;0.001</td><td>&lt;0.001</td></tr></tbody></table>
<table><caption>TABLE 5 The relationship between personal social capital and European Five Dimensions Questionnaire Visual Analogue Scale (EQ-VAS).</caption><thead><tr><th rowspan="2"></th><th rowspan="2"></th><th colspan="2">Total</th><th colspan="2">Rural</th><th colspan="2">Urban</th></tr><tr><th>B (95% CI)</th><th>S.E</th><th>B (95% CI)</th><th>S.E</th><th>B (95% CI)</th><th>S.E</th></tr></thead><tbody><tr><td rowspan="2">Model 1</td><td>BOC</td><td>0.888 (0.714-1.062)***</td><td>0.089</td><td>0.977 (0.75-1.21)***</td><td>0.116</td><td>0.752 (0.47-1.03)***</td><td>0.144</td></tr><tr><td>BRC</td><td>0.425 (0.273-0.577)***</td><td>0.078</td><td>0.118 (-0.10-0.32)</td><td>0.109</td><td>0.697 (0.47-0.92)**</td><td>0.115</td></tr><tr><td rowspan="2">Model 2</td><td>BOC</td><td>0.722 (0.545-0.899)***</td><td>0.090</td><td>0.78 (0.55-1.01)***</td><td>0.118</td><td>0.638 (0.35-0.92)***</td><td>0.146</td></tr><tr><td>BRC</td><td>0.451 (0.300-0.603)***</td><td>0.077</td><td>0.192 (-0.02-0.41)</td><td>0.109</td><td>0.689 (0.46-0.92)***</td><td>0.115</td></tr><tr><td rowspan="2">Model 3</td><td>BOC</td><td>0.699 (0.517-0.882)***</td><td>0.093</td><td>0.741 (0.50-0.98)***</td><td>0.122</td><td>0.672 (0.38-0.97)***</td><td>0.151</td></tr><tr><td>BRC</td><td>0.385 (0.231-0.539)***</td><td>0.079</td><td>0.266 (0.05-0.48)*</td><td>0.110</td><td>0.568 (0.34-0.80)***</td><td>0.118</td></tr><tr><td rowspan="2">Model 4</td><td>BOC</td><td>0.734 (0.557-0.911)***</td><td>0.090</td><td>0.791 (0.56-1.02)***</td><td>0.119</td><td>0.705 (0.43-0.98)***</td><td>0.142</td></tr><tr><td>BRC</td><td>0.252 (0.099-0.405)**</td><td>0.078</td><td>0.042 (-0.17-0.25)</td><td>0.110</td><td>0.414 (0.19-0.64)***</td><td>0.116</td></tr><tr><td rowspan="2">Model 5</td><td>BOC</td><td>0.568 (0.383-0.753)***</td><td>0.094</td><td>0.567 (0.32-0.81)***</td><td>0.124</td><td>0.614 (0.32-0.91)***</td><td>0.151</td></tr><tr><td>BRC</td><td>0.262 (0.106-0.418)**</td><td>0.079</td><td>0.209 (-0.01-0.43)</td><td>0.112</td><td>0.349 (0.12-0.58)**</td><td>0.119</td></tr></tbody></table>

model 5, the BOC was positively correlated with the EQ-5D utility score of the older people (β = 0.0065, 95% CI = 0.0041-0.0090), while the BRC was negatively correlated with the EQ-5D utility score of the older people (β = -0.0035, 95%-CI = -0.0057-0.0013).

For the urban sample: in Model 1, both the BOC (β = 0.0069, 95% CI = 0.0035-0.0103) and the BRC (β = 0.0076, 95% CI = 0.0048-0.0105) were positively correlated with the utility score of the older people. After adjusting for sociodemographic characteristics, socioeconomic status, number of chronic diseases, and healthy behaviors, in model 5, both the BOC (β = 0.0043, 95% CI = 0.0007-0.0078) and the BRC (β = 0.0031, 95% CI = 0.0003-0.0060) were still positively correlated with the EQ-5D utility score of the elderly.

Discussion

To our knowledge, this is the first study that measures the relationship between personal social capital and HRQoL among Chinese older people from an urban-rural perspective. This cross-sectional study found that personal social capital was significantly associated with HRQoL among rural and urban older people. Moreover, the correlation still existed after adjusting the sociodemographic characteristics, socioeconomic status, number of chronic diseases, and healthy behaviors.

Our data showed that the total score of PSC among older people in rural areas was significantly lower than those in urban areas, which was different from a previous study (43). We speculated that the Chinese urban-rural dual structure might cause the discrepancy. In Chinese traditional culture, rural areas are more likely to be an “acquaintance society” than urban areas. On the one hand, with the development

of urbanization, the young migrate to urban areas for work; on the other hand, the intimates and friends of older people start to die, resulting in the PSC of the older people in rural areas are gradually losing (44). In addition, our study also found that BRC among older people was lower than BOC both in urban and rural areas. According to the definition of BRC and BOC, it means that rural and urban areas were facing the dilemma of community or village hollowing out (45). Because there is less social participation and lacking organizations/groups that could provide community public services such as medical services and cultural services for older people (46), which may make them feel less BRC than BOC subjectively. Therefore, the government must encourage the community or village to provide services for the older people by establishing more care facilities and volunteer organizations/groups which can improve the bridge social capital of the older people and give them a sense of belonging in these social organizations or groups. Consistent with previous studies (47, 48), pain/discomfort and anxiety/depression were the most significant health problems affecting older people. In this study, the average EQ-VAS score of the older people was 76.01 ± 14.99, lower than the result of the Fifth National Health Service Survey (80.91 ± 13.7) (49), indicating that the older people were not optimistic about their self-rated health. The average utility index score was 0.9323 ± 0.1016, lower than the fifth National Health Service Survey (0.985 ± 0.056) (39), indicating the urgency of further HRQoL improvement among older adults. In addition, our study found that the utility index score of the rural sample was lower than urban samples, which calls for more attention to the HRQoL of the rural older people.

<footnote>*p < 0.05, **p < 0.01, ***p < 0.001; S.E, standard error. For the urban and rural samples: Model 1: the crude model of BOC and BRC, R = 0.315, R² = 0.099; Model 2: adjusted for sociodemographic characteristics (gender, age, marital status), R = 0.350, R² = 0.122; Model 3: adjusted for socioeconomic status (family income per capita, educational level, employment), R = 0.372, R² = 0.139; Model 4: adjusted for the number of chronic diseases and healthy behaviors (number of chronic diseases, smoking, drinking, regular exercise), R = 0.409, R² = 0.167; Model 5: adjusted for sociodemographic characteristics, socioeconomic status, number of chronic diseases, and healthy behaviors, R = 0.460, R² = 0.212.</footnote>
<table><caption>TABLE 6 The relationship between personal social capital and utility index.</caption><thead><tr><th rowspan="2"></th><th rowspan="2"></th><th colspan="3">Total</th><th colspan="3">Rural areas</th><th colspan="3">Urban areas</th></tr><tr><th>β</th><th>S.E</th><th>95% CI</th><th>β</th><th>S.E</th><th>95% CI</th><th>β</th><th>S.E</th><th>95% CI</th></tr></thead><tbody><tr><td rowspan="2">Model 1</td><td>BOC</td><td>0.0009***</td><td>0.0010</td><td>0.00073-0.0111</td><td>0.0111***</td><td>0.0012</td><td>0.0089-0.0134</td><td>0.0069***</td><td>0.0018</td><td>0.0035-0.0103</td></tr><tr><td>BRC</td><td>0.0015*</td><td>0.0009</td><td>0.0001-0.0003</td><td>-0.0039***</td><td>0.0011</td><td>-0.0062 to -0.0017</td><td>0.0076***</td><td>0.0014</td><td>0.0048-0.0105</td></tr><tr><td rowspan="2">Model 2</td><td>BOC</td><td>0.0068***</td><td>0.010</td><td>0.0049-0.0087</td><td>0.0086***</td><td>0.0012</td><td>0.0062-0.0109</td><td>0.0041*</td><td>0.0017</td><td>0.0006-0.0076</td></tr><tr><td>BRC</td><td>0.0021*</td><td>0.0008</td><td>0.0004-0.0037</td><td>-0.0027*</td><td>0.0011</td><td>-0.0049 to -0.0006</td><td>0.0074***</td><td>0.0014</td><td>0.0045-0.0102</td></tr><tr><td rowspan="2">Model 3</td><td>BOC</td><td>0.0067***</td><td>0.0010</td><td>0.0047-0.0086</td><td>0.0082***</td><td>0.0012</td><td>0.0058 to -0.0106</td><td>0.0054**</td><td>0.0018</td><td>0.0018-0.0090</td></tr><tr><td>BRC</td><td>0.0015*</td><td>0.0009</td><td>0.0002-0.0032</td><td>-0.0023*</td><td>0.0011</td><td>-0.0045 to -0.0001</td><td>0.0066***</td><td>0.0014</td><td>0.0038-0.0095</td></tr><tr><td rowspan="2">Model 4</td><td>BOC</td><td>0.0081***</td><td>0.0010</td><td>0.0061-0.0099</td><td>0.0098***</td><td>0.0012</td><td>0.0074-0.0122</td><td>0.0064***</td><td>0.0017</td><td>0.0030-0.0098</td></tr><tr><td>BRC</td><td>-0.0011*</td><td>0.0008</td><td>-0.0028 to -0.005</td><td>-0.0056***</td><td>0.0011</td><td>-0.0078 to -0.0033</td><td>0.0040**</td><td>0.0014</td><td>0.0012-0.0068</td></tr><tr><td rowspan="2">Model 5</td><td>BOC</td><td>0.0051***</td><td>0.0010</td><td>0.0031-0.0071</td><td>0.0065***</td><td>0.0012</td><td>0.0041-0.0090</td><td>0.0043*</td><td>0.0017</td><td>0.0007-0.0078</td></tr><tr><td>BRC</td><td>-0.0008*</td><td>0.0009</td><td>-0.0024 to -0.0009</td><td>-0.0035**</td><td>0.0011</td><td>-0.0057 to -0.0013</td><td>0.0031*</td><td>0.0014</td><td>0.0003-0.0060</td></tr></tbody></table>

Our results showed that only BOC was positively correlated with self-rated health for the rural samples. Understandably, neighborhood mutual assistance is a normal situation, even in the current rapid economic and social development, this tradition has not died out in rural areas. Rural older people were affected by traditional values, which emphasize more on the family relationship that emphasizes the family relationship more than in urban areas. At the same time, family relationships play an essential role in the health of family members. Moreover, Stafford et al. (50) found that neighborhood relationships' cohesion is positively related to self-rated health. Therefore, older people with more BOC have a higher level of self-rated fitness. Given that most older people in rural areas were engaged in agricultural production activities and lacked social organizations or groups, their communication scope was narrow and social participation was low (51). Another reason is that the self-esteem of rural older people is high (52), leading them to be unwilling to resort to help from social organizations/groups when experiencing health issues, which may also result in the insignificant relationship between BRC and self-rated health. In our study, both BOC and BRC were associated with self-rated health among urban older people. A previous study has proven that good interpersonal relationships and more social participation can improve the health of old citizens (53). Compared with the rural older people, the urban older people had a higher socioeconomic status and more resources to cope with adversity, which could increase their mutual communication, exchange and support, got help and encouragement from others, met their needs of economic and emotional support, relieving psychological pressure, and provided indirect protection for health (54, 55).

Our study indicated that the BOC was positively correlated with the EQ-5D utility index of all older people in rural and

urban areas. That might be related to Chinese Confucianism's filial piety and family culture (56). Traditional values have a deep-rooted influence on the Chinese, especially older people. They attach more importance to their relationship with their family, relatives, and friends (57), the support and reciprocal network provided by people close to them and their living environment had a significant role in meeting their psychological and emotional needs and promoting the quality of life of the older people (58-60). Interestingly, the BRC was negatively correlated with the utility score of the rural older people, while it was positively correlated with the EQ-5D utility score of the urban older people. The BRC was generated from the weak network between the older people and the surrounding social organizations or groups. It improved the actual value of interpersonal communication among older people through individual participation in social activities (61). Compared with older people in urban areas, due to the influence of factors such as traffic, economic conditions, ideas, and consciousness, the rural older people had relatively weak connections with the outside world, rarely participated in social activities, and had a relatively simple social network, with limited help resources available. Urban older people could get help from communities and various social organizations. In addition, as more and more older people started to use smartphones and the Internet (62, 63), especially urban older people, they had more channels to contact the outside world and obtain information. Organizational participation and citizen participation can not only help the elderly to obtain a sense of belonging and self-worth and even directly promote their physical exercise, which is conducive to health promotion. Therefore, it is suggested that government should provide social assistance for older people in multi-levels and various forms; increase health education and promotion in healthy aging; and improve physical facilities, expand coverage of old-age care.

<footnote>*p < 0.05, **p < 0.01, ***p < 0.001; S.E, standard error. For the urban and rural samples: Model 1: the crude model of BOC and BRC, Prob > Chi²; Model 2: adjusted for sociodemographic characteristics (gender, age, marital status), Prob > Chi²; Model 3: adjusted for socioeconomic status (family income per capita, educational level, employment), Prob > Chi²; Model 4: adjusted for the number of chronic diseases and healthy behaviors (number of chronic diseases, smoking, drinking, regular exercise), Prob > Chi²; Model 5: adjusted for sociodemographic characteristics, socioeconomic status, number of chronic diseases, and healthy behaviors, Prob > Chi².</footnote>
## Limitations

There are still several limitations in our study. First, this is a cross-sectional study, which can only reflect the association between PSC and the HRQoL among older Chinese people. Therefore, causality cannot be determined. Second, this study is based on self-reported questionnaires, leading to some bias due to inaccurate responses. Third, the concept and measurement of PSC are still controversial. Though there are many ways to measure social capital, each instrument has its limitations and cannot cover all areas of social capital. For future studies, all the limitations should try to avoid. Four, we did not consider the regional and economical difference, although we conducted the survey in Henan province, Hubei province, Fujian province, Jiangsu province, etc., because we used the convenient sampling, indicating the limited representation of samples.

## Conclusion

In Conclusion, our study found that (1) the PSC of the older people needs to improve further, and the PSC level of the rural older people was lower than that of the urban areas. (2) Pain/discomfort and anxiety/depression were the most significant health problems affecting older people. Older people in rural areas were more likely to have problems than older people in urban areas, and the level of health of rural older people was worse than urban older people. (3) The PSC of the older people was related to the HRQoL. The BOC was positive associated with the rural older people's HRQoL, while the BRC was negatively associated with the rural older people's HRQoL. BOC and the BRC were both positively correlated with the HRQoL of urban older people. Therefore, to improve the HRQoL of the older people, we should increase the BOC of the elderly in rural areas, and the BOC and BRC of the elderly in urban areas.

## Data availability statement

The original contributions presented in this study are all included in the article. Further inquiries can be directed to the first or corresponding authors. The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

## Ethics statement

The studies involving human participants were reviewed and approved by the Institutional Review Board, School of Public Health, and Faculty of Medical Sciences, Wuhan University. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.

## Author contributions

QW and DJ contributed to the study conception and design. DJ, YY, and HZ performed the material preparation, data collection, and analysis. DJ and YY wrote the first draft of the manuscript. QW revised and edited the draft. All authors commented on previous versions of the manuscript, read, and approved the final manuscript.

## Acknowledgments

The Global Health Institute of Wuhan University lead the study. We thank Zhaoxun Hou from Harvard University for English language editing.

## Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

## Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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<figure><img src="image_7.png" /></figure>

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EDITED BY

Roberta Zupo,
National Institute of Gastroenterology
S.de Bellis Research Hospital
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Oluwaseun Ariyo,
University of Ibadan, Nigeria
Sajama Nepali,
Local Initiatives for Biodiversity,
Research and Development, Nepal

*CORRESPONDENCE

Nigusie Shifera

<figure><img src="image_8.png" /></figure>

nigusieshifera@gmail.com

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CITATION

Shifera N, Endale A, Debela D and
Yosef T (2022) Acute malnutrition and
its contributing factors among children
under-five years in rural kebeles of
Shashemene Oromia, Ethiopia.
Front. Nutr. 9:1053928.
doi: 10.3389/fnut.2022.1053928

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not comply with these terms.

Acute malnutrition and its
contributing factors among
children under-five years in rural
kebeles of Shashemene Oromia,
Ethiopia

Nigusie Shifera¹*, Aschalew Endale², Degfachew Debela³ and Tewodros Yosef¹

¹School of Public Health, College of Medicine and Health Sciences, Mizan Tepi University, Mizan Teferi, Ethiopia, ²Public Health Department, Arsi University, Asella, Ethiopia, ³Public Health Emergency Department, Ethiopian Public Health Institutions, Addis Ababa, Ethiopia

**Introduction:** Globally, more than 52 million under-five years old were wasted; One-third of these children live in Africa. Ethiopia is the seventh country among the ten top countries in which acute malnutrition (AM) is concentrated and currently 10% of under-five children are wasted. Even though Ethiopia has implemented a variety of nutritional interventions, acute malnutrition is still prevalent and spreading at an alarming rate. Therefore, this study aimed to assess the prevalence of acute malnutrition and its contributing factors among children under-five years of age.

**Materials and methods:** A community-based cross-sectional study was conducted from July 1 to 30, 2018 among 12 randomly selected kebeles. The sample sizes were proportionally allocated to the selected kebeles. A total of 457 mothers/caretakers of under-five children were interviewed using pre-tested structured questionnaires and anthropometric measurements of the children were taken using standard procedures. EPI data version 4.2 was used for data entry and Statistical Package for the Social Sciences (SPSS) Version 21 was used for statistical analysis. The World Health Organization (WHO) Anthro software was used to convert nutritional data indices. Binary logistic regression was used to determine the association between dependent and independent variables. The level of significance was declared at a P-value <0.05.

**Results:** The prevalence of acute malnutrition is 19.91% (95%CI; 16.24%, 23.57%) among under-five children. Factors contributing to acute malnutrition were mothers with no antenatal care (ANC) visits [adjusted odds ratio (AOR) = 2.26, 95% CI 1.14–4.46], mothers who had no autonomy in decision-making (AOR = 2.42, 95% CI 1.42–4.12), children with diarrheal disease in the last 2 weeks preceding the survey (AOR = 2.07, 95% CI 1.19–3.59), and not feeding colostrum (AOR = 1.99, 95% CI 1.07–3.71).

**Conclusion and recommendation:** The prevalence of acute malnutrition is high as compared to other findings in Ethiopia. Moreover, decision-making power, not feeding colostrum, no ANC visit, and a child's history of diarrhea were independent determinants of acute malnutrition. Therefore, the local
health department and health extension workers should consider imparting
health education for women on nutritional counseling and timely treatment
for children with diarrhea. Empowering women's decision-making is also a key
element in addressing wasting among under-five children.

KEYWORDS

prevalence, acute malnutrition, children, rural, Ethiopia

Introduction

Malnutrition is the lack of the right kinds of food and
nutrients needed by the body for appropriate growth and
development. It includes undernutrition and overnutrition.
Stunting, wasting, and being underweight are classified as
among Anthropometric indicators commonly used to measure
malnutrition in a population of under-five children (1). Acute
malnutrition (AM) is classified into severe acute malnutrition
(SAM) and moderate acute malnutrition (MAM) according to
the degree of wasting and the presence of edema (2).

Acute malnutrition develops as a result of rapid weight loss or a failure to gain weight. In children, it is assessed via the nutritional index of Weight-for-height, Mid-Upper Arm Circumference (MUAC), and edema is used to decide acute malnourishment. If a child's weight for height measurement is <70% of the normal range for his age, or when the child's MUAC is <11 cm, then the child would be diagnosed with severe acute malnutrition. The presence of one criterion is sufficient to categorize a child as severely malnourished (3). Successful management of severe acute malnutrition requires a medical intervention combined with a community-based approach to help with the timely detection and appropriate provision of treatment (4).

Globally in 2015 about 480,000 under-five children continue to die every month. Undernutrition causes deaths in 53% of children under-five years, acute malnutrition is responsible for more than 28% (5), and around one million children under the age of five years die every year from severe acute malnutrition (6). Globally, about 52 million under-five years old were wasted and among this around 32% (16 million) were severely wasted (7), one-third of these children live in Africa, especially in Sub-Saharan Africa (SSA) (8).

Abbreviations: AM, acute malnutrition; ANC, antenatal care; AOR,
adjusted odds ratio; BMI, body mass index; CI, confidence interval; CF,
complementary feeding; COR, crude odds ratio; DDS, dietary diversity
score; EBF, exclusive breast feeding; EDHS, Ethiopian Demographic
and Health Survey; EFY, Ethiopian fiscal year; ETB, Ethiopian Birr; HH,
households; HI, health institution; MAM, moderate acute malnutrition;
MUAC, Mid-Upper Arm Circumference; SAM, severe acute malnutrition;
SD, standard deviation; SDG, sustainable development goals; SSA, Sub-
Saharan Africa; SPSS, Statistical Package for the Social Sciences; WHO,
World Health Organization; W/H, weight/height.

In Ethiopia; the 2016 Ethiopian Demographic and Health Survey (EDHS) revealed that there has been an improvement in the nutritional status of children. The percentage of stunting fell from 40% in 2014 to 38% in 2016. Similarly, the percentage of underweight children declined from 25% to 24%, but the prevalence of wasting increased from 9% to 10%. Furthermore, the prevalence of wasting is different from region to region for instance in Oromia's national regional state, the percentage of children who were wasted is 10.6% and in the Somali regional state was 22.7% (9). Recent studies conducted in Kamashi district and Seqota district, Amhara region, Ethiopia revealed acute undernutrition accounted for 4.3% and 8%, respectively (10, 11).

Currently, there are different nutritional interventional
programs and activities being held by the government
concerned with policy-making and implementation programs
like the Community based Nutrition Program (CBN), Essential
Nutrition Action (ENA), Community Health Day (CHD),
Infant and Young Child Feeding program (IYCF), Community
Management of Acute Malnutrition (CMAM). In line with the
government's role, there are also different local and international
NGOs working in Ethiopia for the overall achievement of the
sustainable development goals (SDG). Despite the entire effort,
the trend of decline in the undernutrition problem is not
satisfactory (12-14).

Even though Ethiopia has been implementing different
nutritional programs, wasting is still the top leading public
health problem for under-five years children (15, 16). Ethiopia
is still the seventh country among the ten top countries in which
wasting (acute malnutrition) is concentrated. Even though, the
previous studies pointed out the prevalence of wasting, most of
these studies do not identify the key determinants of wasting
among the rural kebele community (17). Therefore, this study
aimed to identify the prevalence of acute malnutrition and its
contributing factors among children under-five years of age in
rural kebeles of Shashemene district, Oromia region, Ethiopia.

Materials and methods

Study setting, design, and period

A community-based cross-sectional study was conducted
from July 1 to 30, 2018 in the Shashemene district of
Oromia, Ethiopia. The district is located 250 km far from
Addis Ababa; the capital city of Ethiopia. The district has 38 rural administrative kebeles with 265,109 total populations in 55,231 households with males to female ratio of 1:1.02. The projected number of under-five children is 43,557 projected from the 2007 national population census (18). Regarding health infrastructures, there are eight health centers, three private clinics, and thirty-eight health posts. The majority of the population was engaged in agricultural activities in the district and the main crops/products were Maize, Teff, and Sorghum, fruits and vegetables, and animal husbandry.

## Populations

All children under-five years living in rural kebeles of sahshemene zone were the source population. Those selected children who lived in the selected households during the study period were the study population. Physically disabled children, ages below-five years, and critically ill children were excluded from the study.

## Sample size determination and sampling procedures

The sample size was determined using single population proportion formula by considering an assumption of 95% confidence level, 5% margin of error, and 23.6% prevalence of acute malnutrition from a previous study done in Hawassa Zuria district, Southern Ethiopia (8).

The following assumption was used.

$$n = (Z_{\alpha/2})^{2*} p (1 - p)^2 / d^2 = \frac{(0.764) (0.236) (1.96)^2}{(0.05)^2} = 277.$$

After considering a design effect of 1.5 and 10% non-response rate compensation; the final sample size became 457.

## Sampling procedures

A multi-stage sampling technique was applied. From the 38 rural kebeles in the Shashemene district, 12 kebeles were selected using simple random sampling. From each kebele, three gotes were again randomly selected. The total sample size was proportionally distributed to each gote based on their population size. A maximum of one child aged younger than five years was included randomly in each selected household.

## Study variables

The dependent variable was acute malnutrition (wasting). The independent variables were socio-demographic characteristics (maternal/parental education,

maternal/parental occupation, family size), child and health-related characteristics (sex, age, morbidity status), child caring practices/nutritional care and practice [exclusive breastfeeding practice, complementary feeding (CF) practice, duration of breastfeeding, immunization status, feeding of sick child] maternal characteristics/obstetric factors (number of children ever born, maternal autonomy in decision making, use of extra food during pregnancy or lactation), environmental health condition (access to latrine, access to safe water, access to health facilities).

## Operational definitions

**Acute malnutrition (wasting):** a child's weight for height measurement is <-2 SD or bilateral pitting edema.

**Bilateral pitting edema:** pitting edema of the feet verified when thumb pressure applied on top of both feet for 3 s leaves a pit (indentation) in the foot after the thumb is lifted (19).

**Critically ill:** a child who has chronic diseases like severe pneumonia, TB, CHF, DM, and HIV/AIDS.

**The dietary diversity score (DDS)** is the sum of the total number of food groups consumed over 24 h before the data collection (20).

**Healthcare-seeking behavior:** healthcare-seeking behavior by the caregiver of the child within 24 h of the onset of symptoms.

**Immunization status:** either vaccinated or not, if possible, look at the vaccination card or ask the mother (caregiver).

**Morbidly status of the child:** morbidity status in the last 2 weeks before the study period (fever, cough, diarrhea).

**Complementary foods** are foods that are required by the child, after 6 months of age, in addition to sustained breastfeeding.

## Data collection procedures (instruments, personnel, measurements)

For face-to-face interviews, a pre-tested and standardized questionnaire with questions adopted and modified from different literature reviews was used to collect data. Trained data collectors were assigned to all gote to collect the data. The vaccination status of the children was assessed by looking at the vaccination card or asking the mother (caretaker).

Anthropometric data were collected through the measurement of the length/height and weight of all children. A Salter hanging spring scale with graduations of 0.1 kg and a capacity of 25 kg were used for measuring the weight of the children aged 6-23 month and a beam scale for children over 24 months of age were used for measuring weight with minimum clothing and no shoes to the nearest 0.1 kg. The recumbent length measurement was taken for children under 2 years of age, then subtract 0.7 cm to convert the result to height while
for children above 2 years stature was measured in a standing position in centimeters to the nearest of 0.1 cm (3). Edema of the feet was diagnosed if a bilateral depression (pitting) remained after the pressure was released (19).

Child dietary diversity score was calculated by summing a total of seven food groups: (1) grains, roots, and tubers; (2) vitamin a-rich fruits and vegetables; (3) dairy products; (4) flesh foods (meat, fish, poultry, and liver or organ meat); (5) other fruits and vegetables; (6) egg; (7) legumes and nuts; consumed over the reference period (24 h before the data collection). For example, if one child eats from each food group, his/her DDS will be 7 (20).

## Data quality assurance

The questionnaire was translated into the local language, Afan Oromo, for the fieldwork and back to English to check its consistency among three health workers who are fluent speakers of Afan Oromo and English. Besides, it was pre-tested in Shalla district in 5% of the sample size. Twelve diploma nurses for data collection and four BSc nurses for supervision were trained for 2 days. The training covered in the questionnaire and the importance of disclosing the possible benefits and purpose of the study to the study participants before starting data collection. The data collection was supervised by the principal investigator. Every questionnaire was supervised and reviewed for completeness and logical consistency. The completeness of the questionnaire was also checked before data entry. Validation of instruments, measurements, and random auditing was done daily. The double data entry would be used to ensure data quality.

## Data processing and analysis

EPI info version 7 software was used for data entry (21) and cleaning and then the data were exported to Statistical Package for the Social Sciences (SPSS) Version 21 for further processing and analysis (22). World Health Organization (WHO) Anthro software was used to convert nutritional data indices from anthropometric measurements into Z-scores. A binary logistic regression model was used to compute the association between each independent variable and dependent variable. Independent variables that show association with outcome during bivariate analysis at a P-value <0.25 were included in multivariable analysis to control for potential confounders. Descriptive analysis was used to describe the percentages and number of distributions of the respondents by socio-demographic characteristics and other relevant variables in the study.

Homer-Lemeshow goodness-of-fit was used to test for the model fitness and a backward step-wise (likelihood ratio) method was also conducted. Adjusted odds ratio (AOR) along

with a 95% confidence interval (CI) was estimated to assess the strength of the association and a P-value <0.05 was considered to declare the statistical significance in the multivariable analysis in this study.

## Ethical consideration

Ethical clearance was obtained from the Institutional Review Board at the College of Health Sciences of Arsi University. In addition, a permission letter from the Shashemene District Health Office was obtained before field activities started, and written informed consent was obtained from the parents/caretakers of the study subjects after explaining the study objective and procedures.

# Results

## Socio-demographic characteristics

A total of 457 under-five children participated with a response rate of 100%. The mean age (SD) of the children and mothers was 25 (3.4) months and 26.9 (4.3) years, respectively. A household headed by a male or a married person accounted for more than 90% of the respondents [433 (94.75%) and 414 (90.59%)], respectively. 14 (90.60%) of the mothers were Oromo, while 366 (80.09%) of the respondents identified as Muslims. In a similar proportion, 240 (52.52%) of the caregivers had formal education and were autonomous in households' decision-making (Table 1).

## Maternal and child characteristics

Of the total children, 206 (45.08%) were delivered at home and 251 (54.92%) children were delivered at health facilities. Concerning vaccination status and vitamin A supplementation, 432 (94.53%) of children were vaccinated and 181 (41.51%) of children were supplemented with vitamin A, respectively. Among the surveyed 457 mothers 432 (94.53%) of mothers/caregivers of children were in the age group <35 years and 395 (86.43%) mothers of under-five children practiced at least one antenatal care (ANC) visit. While 135 (29.54%) and 118 (25.82%) mothers did not take extra meals during pregnancy and lactation, respectively (Table 2).

## Child caring practice

Slightly above two-thirds 320 (70.02%) of children had breastfeeding that was initiated within 1 h after birth and about 90 (19.69%) of children took pre-lacteal feeding. About 281 (61.49%) mothers exclusively breastfeed their children for the
<table><caption>TABLE 1 Socio-demographic characteristics of study participants in Shashemene district, Oromia region, Ethiopia, July 2018 (457).</caption><thead><tr><th rowspan="2">Variables</th><th rowspan="2">Categories</th><th colspan="2">Total</th></tr><tr><th>Frequency</th><th>%</th></tr></thead><tbody><tr><td rowspan="2">Head of the HH</td><td>Male</td><td>433</td><td>94.75</td></tr><tr><td>Female</td><td>24</td><td>5.25</td></tr><tr><td rowspan="3">Ethnicity</td><td>Oromo</td><td>414</td><td>90.60</td></tr><tr><td>Wolayita</td><td>30</td><td>6.56</td></tr><tr><td>Others*</td><td>13</td><td>2.84</td></tr><tr><td rowspan="4">Religion</td><td>Muslim</td><td>366</td><td>80.09</td></tr><tr><td>Protestant</td><td>42</td><td>9.19</td></tr><tr><td>Orthodox</td><td>32</td><td>7.00</td></tr><tr><td>Catholic</td><td>17</td><td>3.72</td></tr><tr><td rowspan="2">Land ownership</td><td>Yes</td><td>384</td><td>84.03</td></tr><tr><td>No</td><td>73</td><td>15.97</td></tr><tr><td rowspan="2">Maternal literacy</td><td>Yes</td><td>240</td><td>52.52</td></tr><tr><td>No</td><td>217</td><td>47.48</td></tr><tr><td rowspan="2">Paternal literacy</td><td>Yes</td><td>275</td><td>60.18</td></tr><tr><td>No</td><td>182</td><td>39.82</td></tr><tr><td rowspan="3">Area of agricultural land</td><td>&lt;1 hectare</td><td>16</td><td>4.17</td></tr><tr><td>1-2 hectare</td><td>342</td><td>89.06</td></tr><tr><td>&gt;3 hectare</td><td>26</td><td>6.77</td></tr><tr><td rowspan="4">Occupation of the mother</td><td>Housewife</td><td>421</td><td>92.12</td></tr><tr><td>Merchant</td><td>27</td><td>5.91</td></tr><tr><td>Other**</td><td>9</td><td>1.97</td></tr><tr><td>Farmer</td><td>374</td><td>81.84</td></tr><tr><td rowspan="2">Occupation of the husband</td><td>Merchant</td><td>60</td><td>13.13</td></tr><tr><td>Other**</td><td>23</td><td>5.03</td></tr><tr><td rowspan="2">Family size</td><td>&le;5</td><td>314</td><td>68.71</td></tr><tr><td>&gt;5</td><td>143</td><td>31.29</td></tr><tr><td rowspan="2">Maternal autonomy</td><td>Yes</td><td>240</td><td>52.52</td></tr><tr><td>No</td><td>217</td><td>47.48</td></tr><tr><td rowspan="3">Marital status</td><td>Married</td><td>414</td><td>90.59</td></tr><tr><td>Divorced</td><td>26</td><td>5.69</td></tr><tr><td>Other***</td><td>17</td><td>3.72</td></tr></tbody></table>

first 6 months. Three-fourth 323 (74.08%) of the children started complementary feeding in addition to breastfeeding at the age of 6 months while 111 (25.46%) of the children started complementary feeding. Spoon feeding was practiced in 240 (55.04%) children while 147 (33.72%) and 49 (11.24%) used

<table><caption>TABLE 2 Maternal and child characteristics of study participants in Shashemene district, Oromia region, Ethiopia, July 2018 (457).</caption><thead><tr><th rowspan="2">Variables</th><th rowspan="2">Categories</th><th colspan="2">Total</th></tr><tr><th>Frequency</th><th>%</th></tr></thead><tbody><tr><td rowspan="2">Age of the mothers/caregivers (years)</td><td>&lt;35</td><td>432</td><td>94.53</td></tr><tr><td>&ge;35</td><td>25</td><td>5.47</td></tr><tr><td rowspan="3">Maternal extra meal during the last pregnancy</td><td>0</td><td>135</td><td>29.54</td></tr><tr><td>1</td><td>234</td><td>51.20</td></tr><tr><td>2</td><td>88</td><td>19.26</td></tr><tr><td rowspan="3">Maternal extra meal during the last lactating</td><td>0</td><td>118</td><td>25.82</td></tr><tr><td>1</td><td>242</td><td>52.95</td></tr><tr><td>2</td><td>97</td><td>21.23</td></tr><tr><td rowspan="2">ANC visit</td><td>Yes</td><td>395</td><td>86.43</td></tr><tr><td>No</td><td>62</td><td>13.57</td></tr><tr><td rowspan="2">Age of the mothers at first birth</td><td>&le;20</td><td>384</td><td>84.03</td></tr><tr><td>&gt;20</td><td>73</td><td>15.97</td></tr><tr><td rowspan="2">Distance from the HH to the HF (min)</td><td>&lt;30</td><td>317</td><td>69.37</td></tr><tr><td>&ge;30</td><td>140</td><td>30.63</td></tr><tr><td rowspan="3">Age of the child (months)</td><td>0-5</td><td>21</td><td>4.60</td></tr><tr><td>6-23</td><td>203</td><td>44.42</td></tr><tr><td>24-59</td><td>233</td><td>50.98</td></tr><tr><td rowspan="2">Sex of the child</td><td>Male</td><td>213</td><td>46.61</td></tr><tr><td>Female</td><td>244</td><td>53.39</td></tr><tr><td rowspan="2">Child received deworming</td><td>Yes</td><td>98</td><td>43.95</td></tr><tr><td>No</td><td>125</td><td>56.05</td></tr><tr><td rowspan="2">The child received vitamin A</td><td>Yes</td><td>181</td><td>41.51</td></tr><tr><td>No</td><td>255</td><td>58.49</td></tr><tr><td rowspan="2">Type of birth</td><td>Single</td><td>440</td><td>96.28</td></tr><tr><td>Multiple/Twins</td><td>17</td><td>3.72</td></tr><tr><td rowspan="2">Child vaccination status</td><td>Yes</td><td>432</td><td>94.53</td></tr><tr><td>No</td><td>25</td><td>5.47</td></tr><tr><td rowspan="2">Place of birth</td><td>Home</td><td>206</td><td>45.08</td></tr><tr><td>HI</td><td>251</td><td>54.92</td></tr><tr><td rowspan="2">History of diarrhea in the last 2 weeks</td><td>Yes</td><td>111</td><td>24.29</td></tr><tr><td>No</td><td>346</td><td>75.71</td></tr><tr><td rowspan="2">History of febrile illness in the last 2 weeks</td><td>Yes</td><td>28</td><td>6.13</td></tr><tr><td>No</td><td>429</td><td>93.87</td></tr></tbody></table>

<footnote>*Amhara, Wolayita, Sidama.</footnote>

<footnote>**Employee, daily laborer.</footnote>

<footnote>***Widowed, separated, single.</footnote>
<table><caption>TABLE 3 Child caring practices of the study population in Shashemene district, Oromia region, Ethiopia, July 2018 (457).</caption><thead><tr><th rowspan="2">Variables</th><th rowspan="2">Categories</th><th colspan="2">Total</th></tr><tr><th>Frequency</th><th>%</th></tr></thead><tbody><tr><td rowspan="4">Duration of breastfeeding</td><td>&lt;6 months</td><td>19</td><td>4.16</td></tr><tr><td>Up to 12 months</td><td>54</td><td>11.81</td></tr><tr><td>For 24 months</td><td>296</td><td>64.77</td></tr><tr><td>More than 24 months</td><td>88</td><td>19.26</td></tr><tr><td rowspan="2">DDS</td><td>&lt;4</td><td>315</td><td>72.25</td></tr><tr><td>≥4</td><td>121</td><td>27.75</td></tr><tr><td rowspan="2">Time of breastfeeding initiation</td><td>Within the first hour</td><td>320</td><td>70.02</td></tr><tr><td>After the first hour of delivery</td><td>137</td><td>29.98</td></tr><tr><td rowspan="2">Child deprive colostrum</td><td>Yes</td><td>81</td><td>17.72</td></tr><tr><td>No</td><td>376</td><td>82.28</td></tr><tr><td rowspan="2">EBF of the child during the first 6 months</td><td>Yes</td><td>281</td><td>61.49</td></tr><tr><td>No</td><td>176</td><td>38.51</td></tr><tr><td rowspan="3">Age of the child while starting CF</td><td>&lt;6 months</td><td>111</td><td>25.46</td></tr><tr><td>At 6 months</td><td>323</td><td>74.08</td></tr><tr><td>6-12 months</td><td>2</td><td>0.46</td></tr><tr><td rowspan="3">Method of feeding</td><td>Bottle</td><td>147</td><td>33.72</td></tr><tr><td>Cup</td><td>49</td><td>11.24</td></tr><tr><td>Spoon</td><td>240</td><td>55.04</td></tr><tr><td rowspan="2">Pre-lacteal feeding</td><td>Yes</td><td>90</td><td>19.69</td></tr><tr><td>No</td><td>367</td><td>80.21</td></tr><tr><td rowspan="2">Frequency of breastfeeding</td><td>4-7</td><td>155</td><td>33.92</td></tr><tr><td>≥8</td><td>302</td><td>66.08</td></tr><tr><td rowspan="2">Increase the pattern of feeding for a sick child</td><td>Yes</td><td>222</td><td>48.58</td></tr><tr><td>No</td><td>235</td><td>51.42</td></tr></tbody></table>

bottle and cup feeding, respectively. In addition, 315 (72.25%) of children were consume diversified food groups <4 within 24 h while 121 (27.75%) of children were consume diversified food groups ≥4 within 24 h (Table 3).

## Environmental factors

About 399 (87.30%) households got water from piped outside the compound. The average water utilized per household was 35 L per day and the average round trip to fetch water

<table><caption>TABLE 4 Environmental characteristics of the study population in Shashemene district, Oromia region, Ethiopia, July 2018 (457).</caption><thead><tr><th rowspan="2">Variables</th><th rowspan="2">Categories</th><th colspan="2">Total</th></tr><tr><th>Frequency</th><th>%</th></tr></thead><tbody><tr><td rowspan="2">Latrine availability</td><td>Yes</td><td>382</td><td>83.59</td></tr><tr><td>No</td><td>75</td><td>16.41</td></tr><tr><td rowspan="2">Domestic liquid waste disposal</td><td>Open field</td><td>274</td><td>59.96</td></tr><tr><td>Pit</td><td>183</td><td>40.04</td></tr><tr><td rowspan="2">HH domestic solid waste disposal</td><td>Open field</td><td>234</td><td>51.20</td></tr><tr><td>Pit</td><td>133</td><td>29.10</td></tr><tr><td rowspan="2">Type of residence house</td><td>Burning</td><td>10</td><td>2.20</td></tr><tr><td>Composting</td><td>80</td><td>17.50</td></tr><tr><td rowspan="2">Kitchen site</td><td>Thatched</td><td>269</td><td>58.86</td></tr><tr><td>Corrugated iron sheet</td><td>188</td><td>41.14</td></tr><tr><td rowspan="2">Live together with animals</td><td>Inside residential house</td><td>140</td><td>30.63</td></tr><tr><td>Separated</td><td>317</td><td>69.37</td></tr><tr><td rowspan="2">Source of drinking water</td><td>Yes</td><td>127</td><td>27.80</td></tr><tr><td>No</td><td>330</td><td>72.20</td></tr><tr><td rowspan="3">Take to HI when the child is sick</td><td>Piped outside the compound</td><td>399</td><td>87.30</td></tr><tr><td>Covered well</td><td>21</td><td>4.60</td></tr><tr><td>Protected spring</td><td>35</td><td>7.66</td></tr><tr><td rowspan="2">Frequency of breastfeeding</td><td>Yes</td><td>235</td><td>51.42</td></tr><tr><td>No</td><td>222</td><td>48.58</td></tr></tbody></table>

from the source of water was 37.24 min. Concerning household domestic liquid and solid waste disposal, 274 (59.96%) and 234 (51.20%) of the households disposed of domestic liquid and solid waste in open fields, respectively, and latrine was not available in 75 (16.41%) households. Regarding the residence house, 269 (58.86%) households had thatched homes while 188 (41.14%) were corrugated iron sheets (Table 4).

## Prevalence of acute malnutrition

The prevalence of acute malnutrition among under-five children was 91 (19.91%) with (95% CI; 16.24%, 23.57%). In children aged 0-5, 6-11, 12-23, 24-35, and 36-47 months, the prevalence of wasting was 3 (14.29%), 26 (26.53%), 30
<figure><img src="image_9.png" /></figure>

(28.57%), 18 (25.71%), 6 (7.31%), and 8 (9.76%), respectively (Figure 1).

## Factors associated with acute malnutrition (wasting)

In bivariate analysis, parental education, maternal autonomy in decision making, squeezed out, and throwing colostrums, ANC visit, history of diarrhea in the last 2 weeks, history of febrile disease in the last 2 weeks, age of mother at first birth, place of birth, maternal education, and pre lacteal feeding were the variables that showed an association with the outcome variable at the bivariate analysis with *P*-value <0.25 were entered into the final multivariable logistic regression to control potential confounders.

On multivariable logistic regression, maternal ANC visits were significant predictors determinants for acute malnutrition, children whose mothers were not on ANC visits at least once were two times more likely to be acutely malnourished than those children whose mothers were on ANC visits at least once (AOR = 2.26, 95% CI 1.14–4.46). The odds of developing wasting were two times higher among children whose mother has not autonomous in decision-making than those whose mother has autonomous in decision-making (AOR = 2.42, 95% CI 1.42–4.12).

The morbidity status of the child by diarrheal disease in the last 2 weeks preceding the survey was also seen as one of the determinants of acute malnutrition in under-five children. Acute malnutrition was two times more likely to occur in children who had diarrhea before 2 weeks than those children who had no diarrhea within 2 weeks (AOR = 2.07, 95% CI 1.19–3.59). Although children who had a fever in the last 2

weeks preceding the survey shows a significant association in bivariate analysis, no association was observed between children who had a fever in the last 2 weeks preceding the survey and wasting in multivariable analysis. Similarly, children whose mothers had squeezed out colostrum were two times more likely to be wasted than under-five children whose mothers had no squeeze-out colostrum (AOR = 1.99, 95% CI 1.07–3.71) (Table 5).

## Discussion

In this study, the prevalence of acute malnutrition (wasting) is 19.91% (95%CI; 16.24%, 23.57%) and maternal autonomy in decision making, squeezing out, and throwing colostrum, ANC visit, history of diarrhea in the last 2 weeks, were the independent factors associated with acute malnutrition.

The above-stated prevalence of acute malnutrition (wasting) was higher as compared with 14.2% in Kwara State, Nigeria (23), and 16.5% in Nakaseke and Nakasongola districts, Uganda (24) from African countries. Moreover, the study is higher than 13.4% in the Bule Hora district (25), 16.7% in Hidabu Abote, North Shewa (26), and 9% in Damot Gale (27), studies in Ethiopia. This difference might be due to the variation in the study methodology used and the great variation between the rural and urban communities. Unlike this study, the previous studies mentioned above were conducted in the urban community.

Although present study showed that the prevalence of wasting was high in the district in comparison with the regional (10.6%) and national prevalence (11%) reported by EDHS 2016 (9). The discrepancy might be due to the sample size compared to that of national data. This finding was
<table><caption>TABLE 5 Factors associated with acute malnutrition (wasting) among children under-five years, of Shashemene district, Oromia region, Ethiopia, and July 2018 (457).</caption><thead><tr><th rowspan="2">Variable</th><th colspan="2">AM (wasting)</th><th rowspan="2">COR (95%, CI)</th><th rowspan="2">AOR (95%, CI)</th><th rowspan="2">P-values for AOR</th></tr><tr><th>Yes</th><th>No</th></tr></thead><tbody><tr><td colspan="6"><strong>Paternal literacy</strong></td></tr><tr><td>Yes</td><td>43</td><td>232</td><td>1</td><td></td><td></td></tr><tr><td>No</td><td>48</td><td>134</td><td>1.93 (1.19-3.03)</td><td>1.36 (0.77-1.98)</td><td>0.453</td></tr><tr><td colspan="6"><strong>Maternal autonomy</strong></td></tr><tr><td>Yes</td><td>39</td><td>201</td><td>1</td><td>1</td><td></td></tr><tr><td>No</td><td>52</td><td>165</td><td>1.62 (1.02-2.58)</td><td>2.42 (1.42-4.12)</td><td><strong>0.001</strong></td></tr><tr><td colspan="6"><strong>ANC visit</strong></td></tr><tr><td>Yes</td><td>71</td><td>324</td><td>1</td><td>1</td><td></td></tr><tr><td>No</td><td>20</td><td>42</td><td>2.17 (1.20-33.92)</td><td>2.26 (1.14-4.46)</td><td><strong>0.019</strong></td></tr><tr><td colspan="6"><strong>History of diarrhea in the last 2 weeks</strong></td></tr><tr><td>Yes</td><td>30</td><td>81</td><td>1.73 (1.05-2.56)</td><td>2.07 (1.19-3.59)</td><td><strong>0.010</strong></td></tr><tr><td>No</td><td>61</td><td>285</td><td>1</td><td>1</td><td></td></tr><tr><td colspan="6"><strong>History of febrile illness in the last 2 weeks</strong></td></tr><tr><td>Yes</td><td>10</td><td>18</td><td>2.39 (1.06-5.37)</td><td>1.94 (0.99-3.74)</td><td>0.067</td></tr><tr><td>No</td><td>81</td><td>348</td><td>1</td><td></td><td></td></tr><tr><td colspan="6"><strong>Child deprive colostrum</strong></td></tr><tr><td>Yes</td><td>28</td><td>53</td><td>2.62 (1.54-4.47)</td><td>1.99 (1.07-3.71)</td><td><strong>0.029</strong></td></tr><tr><td>No</td><td>63</td><td>313</td><td>1</td><td>1</td><td></td></tr><tr><td colspan="6"><strong>Age of the mother at first birth</strong></td></tr><tr><td>≤20</td><td>68</td><td>316</td><td>1</td><td></td><td></td></tr><tr><td>>20</td><td>23</td><td>50</td><td>2.14 (1.22-3.74)</td><td>1.93 (0.95-2.74)</td><td>0.216</td></tr><tr><td colspan="6"><strong>Place of birth</strong></td></tr><tr><td>Home</td><td>50</td><td>156</td><td>1.64 (1.03-2.61)</td><td>1.42 (0.86-2.56)</td><td>0.432</td></tr><tr><td>HI</td><td>41</td><td>210</td><td>1</td><td>1</td><td></td></tr><tr><td colspan="6"><strong>Maternal illiteracy</strong></td></tr><tr><td>Yes</td><td>37</td><td>203</td><td>1</td><td>1</td><td></td></tr><tr><td>No</td><td>54</td><td>163</td><td>1.82 (1.14-2.89)</td><td>1.67 (0.87-2.95)</td><td>0.322</td></tr><tr><td colspan="6"><strong>Pre-lacteal feeding</strong></td></tr><tr><td>Yes</td><td>31</td><td>59</td><td>2.69 (1.61-4.50)</td><td></td><td></td></tr></tbody></table>

(Continued)

<table><caption>TABLE 5 (Continued)</caption><thead><tr><th rowspan="2">Variable</th><th colspan="2">AM (wasting)</th><th rowspan="2">COR (95%, CI)</th><th rowspan="2">AOR (95%, CI)</th><th rowspan="2">P-values for AOR</th></tr><tr><th>Yes</th><th>No</th></tr></thead><tbody><tr><td>No</td><td>60</td><td>307</td><td>1</td><td></td><td></td></tr><tr><td colspan="6"><strong>Maternal extra meals during lactating</strong></td></tr><tr><td>0</td><td>27</td><td>91</td><td>2.90 (1.29-6.52)</td><td>2.14 (0.97-3.74)</td><td>0.065</td></tr><tr><td>1</td><td>55</td><td>187</td><td>2.88 (1.36-6.08)</td><td>2.04 (0.88-2.74)</td><td>0.087</td></tr><tr><td>2</td><td>9</td><td>88</td><td>1</td><td></td><td></td></tr></tbody></table>

consistent with 23.6% in Hawassa Zuria district, Southern Ethiopia (28). This similarity may be due to socioeconomic, study design, and sample size. However, this finding was lower than 42.3% in Dolo Ado district, Somali region, Ethiopia (29). This may be due to Dolo Ado district being highly affected by drought and the community being pastoralists. Moreover, the finding was lower than 25% in Christ specialist hospital, Ogui, Enuu, Nigeria, and higher than 14.8% at the University of Nairobi, Kenya (30, 31). This difference might be due to the study design, sociodemographic characteristics, lifestyle, and cultural variation.

Maternal ANC visit was a significant determinant of acute malnutrition. Children whose mothers had no ANC visits were two times more likely to develop acute malnourished than those children whose mother has ANC visits. Since the overall aim of ANC is to produce a healthy mother and baby at the end of pregnancy, taking antenatal visits may help the mother and child to have better health and knowledge of the child-caring practice. This finding was consistent with a study conducted in Damot Gale district, South Ethiopia (27), which revealed that children whose mothers attended ANC were associated with acute malnutrition. However, another study conducted in Shone district, Hadiya zone, SNNPR (32), showed that children whose mothers haven't had antenatal care follow-up are associated with acute malnutrition of the children.

Children whose mothers were not autonomous in decision-making were two times more likely to be wasted than those children whose mothers were autonomous in decision-making. This result was consistent with studies conducted in Shashogo district and Machakel district (33, 34), and studies in Ethiopia. Such findings will require women's autonomy to participate in the decision-making process of the household equally with their husbands. Nowadays the policy of government also supports empowering women, and women's education and the increasing influence of women have a significant impact on the health of the family and community.
Children with diarrhea preceding 2 weeks before the onset of acute malnutrition are significantly associated with acute malnutrition (wasting) children. This can be due to excessive loss of fluids and electrolytes, loss of appetite, or lack of absorption of food in the intestine due to high motility of the intestine during diarrhea episodes (5). This finding was supported by studies done by Shibru and Tesfaye and Bantamen et al. (32, 33).

Squeezing out of first milk was significantly associated with acute malnutrition in under-five children. Children deprived of colostrum were two times more likely wasted than those children who have not deprived of colostrum. This result was consistent with a case-control study conducted in the Machakel district revealed that squeezing out of first milk showed a significant association with malnutrition (33). Exclusive breastfeeding is recommended because breast milk (colostrum) is containing necessary nutrients in the first few months of life. In addition, the mother's antibodies in breast milk provide the infant with immunity to disease. Early supplementation exposes infants to pathogens and thus increases their risk of infection, especially diarrheal disease and it decreases infants' intake of breast milk and therefore suckling, which in turn reduces breast milk production (35).

## Strengths and limitations

The study considers a high-risk population (children 0-5 months of age group), using World Health Organization (WHO) Anthro software and close supervision throughout the field activities were the strength of the study. However, recall bias for some variables (for example DDS, frequency of breastfeeding, complementary feeding) is a limitation of the study.

## Conclusion and recommendation

The prevalence rate of acute malnutrition (wasting) among under-five children in the district was high. Antenatal care visits, decision-making, squeezing out and throwing colostrum, and children who had diarrheal disease were the independent predictors of acute malnutrition. Therefore, the local health department and health extension workers should consider

imparting health education for women on nutritional counseling and timely treatment for children with diarrhea. Moreover, the health departments try to consider the effects of these factors (women's decision-making, benefits of colostrum, diarrheal disease, and ANC) while proving services on nutrition.

## Data availability statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author/s.

## Ethics statement

The studies involving human participants were reviewed and approved by Mizan Tepi University. The patients/participants provided their written informed consent to participate in this study.

## Author contributions

All authors wrote the protocol, participated in data collection, analyzed the data, and wrote the manuscript. All authors contributed to the article and approved the submitted version.

## Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

## Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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<figure><img src="image_10.png" /></figure>

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## OPEN ACCESS

**EDITED BY**
Roberta Zupo,
National Institute of Gastroenterology S. de Bellis Research Hospital (IRCCS), Italy

**REVIEWED BY**
Azam Doustmohammadian,
Iran University of Medical Sciences, Iran
Cézane Reuter,
Universidade de Santa Cruz do Sul, Brazil

*CORRESPONDENCE
Mohammad Redwanul Islam
✉ mohammadredwanul.islam@kbh.uu.se

**SPECIALTY SECTION**
This article was submitted to Nutritional Epidemiology, a section of the journal Frontiers in Nutrition

RECEIVED 30 September 2022

ACCEPTED 05 January 2023

PUBLISHED 25 January 2023

**CITATION**
Islam MR, Rahman SM, Selling K, Näsänen-Gilmore P, Kippler M, Kajantie E, Rahman A, Pervin J and Ekström E-C (2023) Dietary patterns and indicators of cardiometabolic risk among rural adolescents: A cross-sectional study at 15-year follow-up of the MINIMat cohort. *Front. Nutr.* 10:1058965. doi: 10.3389/fnut.2023.1058965

**COPYRIGHT**
© 2023 Islam, Rahman, Selling, Näsänen-Gilmore, Kippler, Kajantie, Rahman, Pervin and Ekström. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Dietary patterns and indicators of cardiometabolic risk among rural adolescents: A cross-sectional study at 15-year follow-up of the MINIMat cohort

Mohammad Redwanul Islam<fnref n="1" />*, Syed Moshfiqur Rahman<fnref n="1" />,², Katarina Selling<fnref n="1" />, Pieta Näsänen-Gilmore³,⁴, Maria Kippler<fnref n="5" />, Eero Kajantie³,⁴, Anisur Rahman<fnref n="1" />,², Jesmin Pervin² and Eva-Charlotte Ekström<fnref n="1" />

**Background:** Diet being a modifiable factor, its relationship with cardiometabolic risk is of public health interest. The vast majority of studies on associations of dietary patterns with cardiometabolic risk indicators among adolescents are from high-income countries and urban settings. We sought to describe dietary patterns and examine their associations with selected cardiometabolic risk indicators—waist circumference (WC), systolic blood pressure, fasting lipid profile and insulin resistance—along with its gender stratification among adolescents in a low-income, rural setting.

**Methods:** This cross-sectional study utilized data from the 15-year follow-up of the Maternal and Infant Nutrition Interventions in Matlab (MINIMat) cohort in southeast Bangladesh. The children who were born as singletons to the mothers randomized in the MINIMat trial and had valid birth anthropometrics were eligible for the follow-up. We employed a single, qualitative 24-hour recall to assess diet. Dietary patterns were derived from simple *K*-means cluster analysis, and calculation of dietary diversity score (DDS) using a validated instrument. Anthropometric parameters and systolic blood pressure were recorded. Fasting plasma triglyceride, total cholesterol, low- and high-density lipoproteins, insulin and glucose levels were measured. We calculated insulin resistance using the Homeostasis Model Assessment equation (HOMA-IR). Three right-skewed outcome variables were natural log (Ln) transformed: WC, triglyceride and HOMA-IR. Omnibus and gender-specific multiple linear regression models were fitted.

**Results:** Among 2,253 adolescents (52.1% girls, 7.1% overweight/obese), we identified four diet clusters: Traditional, Fish-dominant, Meat-dominant, and High-variety. No significant associations were found between the clusters and indicators. On gender-stratification, triglyceride levels were lower among boys in the Fish-dominant (Ln-triglyceride $β_{adjusted}$: -0.09; 95% confidence interval (CI): -0.15, -0.02) and Meat-dominant (Ln-triglyceride $β_{adjusted}$: -0.08; 95% CI: -0.15, -0.004) clusters than among boys in the Traditional cluster. Compared to boys in the bottom quartile of

<footnote n="1">¹Department of Women's and Children's Health, Uppsala University, Uppsala, Sweden, ²Maternal and Child Health Division, International Centre for Diarrhoeal Disease Research, Bangladesh (icddr,b), Dhaka, Bangladesh, ³Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland, ⁴PEDEGO Research Unit, MRC Oulu, Oulu University Hospital, University of Oulu, Oulu, Finland,</footnote>

<footnote n="5">⁵Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden</footnote>
DDS, boys in the top quartile had 2.1 mm of Hg (95% CI: 0.5, 3.6) higher systolic blood pressure and 1.9% (95% CI: 0.01–3.8%) higher WC.

**Conclusion:** While statistically significant, the gender-specific differences in triglyceride, systolic blood pressure, and waist circumference across dietary patterns were small. Associations between dietary patterns and cardiometabolic risk indicators may require a time lag beyond mid-adolescence to manifest in a rural setting. Prospective studies are warranted to delineate the magnitude and direction of those associations.

KEYWORDS

dietary patterns, cardiometabolic risk markers, blood pressure, lipid profile, waist circumference, rural adolescents, low- and middle-income country (LMIC), Bangladesh

Introduction

Elevated systolic blood pressure (SBP) and raised blood levels of glucose and lipids pose a major challenge to global health. The Global Burden of Disease (GBD) 2019 study (1) examined the morbidity and mortality attributable to these cardiometabolic risks. SBP > 115 mm of Hg accounted for an estimated 10.8 million deaths worldwide in 2019 (1). The corresponding estimates for fasting plasma glucose > 5.4 mmol/L and low-density lipoprotein (LDL) > 1.3 mmol/L were 6.2 million and 2.4 million deaths, respectively (1). Over the past decade, the population exposure to cardiometabolic risks has increased sharply in low- and middle-income countries (LMICs). For instance, between 2010 and 2019, the age-standardized, risk-weighted prevalence of high fasting plasma glucose increased by about 2.5% annually in lower-middle income countries. In the same period, the annual rate of increase was 1.3% globally and 1.8% in high-income countries (1). The escalating exposure in LMICs coincides with rapid nutrition transition and changes in population diets due to widening availability of unhealthy foods (2). Diet is a modifiable factor intricately related to these cardiometabolic risks (3). Hence, exploring dietary patterns (DPs) in relation to indicators of cardiometabolic risk is relevant to understanding how habitual diet shapes cardiometabolic risk profile in different populations.

Cardiometabolic risk profile emerging in adolescence may track into adulthood. Epidemiological studies have shown that adverse cardiometabolic risk profile in adolescence predicts hypertension, type 2 diabetes, dyslipidemias and cardiovascular events decades later in the adulthood (4–6). Similarly, food choices and DPs established in adolescence have been found to persist well into adulthood (7, 8). Therefore, identification of healthful or deleterious DPs in terms of cardiometabolic risk among adolescents could inform non-communicable disease prevention strategies. Nevertheless, the vast majority of the studies investigating associations between DPs and indicators of cardiometabolic risk among adolescents are from high-income countries (9–14) or urban settings of middle-income countries (15–19). These studies are unlikely to have captured the potentially higher biological susceptibility (20) from early-life undernutrition (21, 22) among rural adolescents of LMICs. Furthermore, the findings are heterogeneous, particularly regarding gender-based differences in association. At 14-year follow-up of an Australian birth cohort, a DP with higher shares of saturated fat, refined sugar and added salt was cross-sectionally associated with

higher total cholesterol (TC) and waist circumference (WC) among girls only (9). Prospective analyses in the same cohort revealed positive associations of an “energy dense, high fat, and low fiber” DP with WC among girls and fasting glucose among boys at 17 years (11). In a cross-sectional study among Tunisian adolescents (n = 1,019, 44.4% rural), a “modern” DP consisting of white bread, high-fat dairy, sugar-sweetened beverage (SSB) and processed meat was positively associated with WC only among the boys (23). These diverse findings indicate the need for studying DPs in relation to cardiometabolic risk among adolescents in resource-limited, rural settings for context-specific empirical evidence.

Bangladesh is a lower-middle-income country in South Asia and home to 36 million adolescents, who represent one-fifth of the population. The majority of these adolescents live in rural areas (24). Yet studies on DPs and cardiometabolic risk among rural adolescents in Bangladesh remain markedly sparse. Aiming to address this gap, we sought to: (i) describe DPs in a rural birth cohort of Bangladeshi adolescents; and (ii) examine the associations of the DPs with WC, SBP, plasma levels of triglyceride (TG), TC, LDL and high-density lipoprotein (HDL), and insulin resistance (IR) along with potential difference in associations by gender.

Materials and methods

Study design, participants, and setting

This cross-sectional study availed data collected during the 15-year follow-up of MINIMat trial, conducted from September 2017 to June 2019. MINIMat (Maternal and Infant Nutrition Interventions in Matlab, reg#ISRCTN16581394) was a community-based, randomized trial that tested the effects of prenatal food and micronutrient supplementation on maternal and birth outcomes (25). Between 2001 and 2003, a total of 4,436 pregnant women from Matlab were randomized. This resulted in 3,267 singleton live births with valid birth anthropometrics, forming the MINIMat cohort that has been intensively followed up (26). The 15-year follow-up comprised three parts: formative phase, household survey, and clinic visit. Trained interviewers with at least 12 years of formal education interviewed the adolescent-mother/guardian dyads at their houses using a pre-tested, structured questionnaire. The clinic visit involved anthropometric assessment and collection of fasting blood sample.
Out of the 3,267 eligible adolescents, 2,465 (75.5%) completed the household survey and 2,300 (70.4%) completed the clinic visit. The participant flow and reasons for loss to follow-up are presented in Figure 1, "Results" section.

Matlab is a rural sub-district, located about 55 km to the southeast of the capital city of Dhaka. The community is agrarian and rice farming is the main occupation in Matlab, but a few villages rely on fishing as the means of income. An agricultural lean period usually occurs from September to November (27).

## Dietary assessment

The foods consumed by Matlab adolescents were listed during the formative phase. Focus groups (28) and findings from previous follow-ups informed the listing process. There were 15 food groups in total, and the items included in each are presented in Supplementary Table 1. The details of the food grouping have been reported elsewhere (29, 30). Briefly, the foods other than ultra-processed and deep-fried foods were arranged into 10 groups using locally adapted version of a validated (31, 32) instrument endorsed by the Food and Agriculture Organization (33). The groups were: (i) grains, white roots and tubers, and plantains; (ii) vitamin A-rich vegetables, tubers and fruits; (iii) dark green leafy vegetables; (iv) other vegetables; (v) other fruits; (vi) meat; (vii) egg; (viii) fish; (ix) legumes, nuts and seeds; and (x) milk products. The ultra-processed foods (UPFs) were identified using the NOVA system proposed by Monteiro et al. (34), and consolidated into four groups: (i) ready-to-eat or "instant" foods; (ii) confectionery, sweets and similar packaged products; (iii) savory snacks, and (iv) sugar-sweetened beverage (SSB) including energy drinks. Foods submerged in heated oil while cooking were grouped as deep-fried foods.

A single, interactive, 24-hour, qualitative recall was employed to assess consumption. Reported consumption of one tablespoonful (~15 grams) or more of ≥1 item(s) belonging to a food group classified the adolescent as a consumer (coded "1"). Those who did not consume received a code of "0" for that food group. The one-tablespoon requirement was used to avoid trivial consumption from inflating the heterogeneity in DPs (35). The enumerators probed for unreported consumption after the initial recall using list and pictorial aid displaying charted photographs of foods. This double-pass approach has been recommended to minimize recall bias (36).

## Assessment of cardiometabolic risk indicators

Trained nurses collected data following a standardized protocol. Body weight was recorded with a digital scale (Tanita BC-418 Body Composition Analyzer, 0.2 kg) and height with a stadiometer (Seca 214, 0.1 cm) while adolescents wore standard light clothes provided by the project and were barefoot. The weight of the clothes (200 gram) were deducted from the measured weight. Body mass index (BMI) was calculated by dividing body weight with height squared (kg/m²). BMI-for-age z-score (BAZ) was calculated using the World Health Organization reference (37). For descriptive purpose, we categorized the adolescents as thin (BAZ < -2), normal-weight (-2 ≤ BAZ ≥ +1), and overweight/obese (BAZ > +1). WC was measured midway between the lower margin of the least palpable rib

and iliac crest with a non-elastic tape (TALC) to the nearest 0.1 cm. Sitting BP was measured in triplicates, at two-minute intervals, using an Omron M10 device after a 10-min seated rest. Venous blood samples (6 ml) after an overnight fast were collected in Lithium-heparin tubes (Sarstedt). The samples were centrifuged; plasma was separated, aliquoted and stored at -70°C. Plasma TC, TG, LDL, and HDL levels were measured in a Cobas Analyzer (Roche) through enzymatic colorimetric assay at the laboratory of the Department of Clinical Chemistry, Skåne University Hospital, Sweden. Plasma insulin level was measured at the icddr,b laboratory. Fasting glucose was measured using the Contour TS Blood Glucose Monitoring System (Bayer) during the clinic visit. We calculated IR using the Homeostasis Model Assessment (HOMA) equation: HOMA-IR = (fasting insulin mU/L × fasting glucose in mmol/L) ÷ 22.5 (38).

## Socio-demographic variables

Gender was a dichotomous variable (girl/boy). An asset score was calculated for each household from principal component analysis (39) of the data on ownership of a set of durables (e.g., mobile phone, television, refrigerator, etc.), access to electricity and sanitary latrine, and nature of fuel used. We constructed a categorical variable (household wealth) by converting asset scores into tertiles: the lowest, intermediate and highest tertiles representing the poorest, middle-status and richest households, respectively. Educational status was categorized according to completed years of formal education: none, primary (1-5 years), and secondary (6-12 years) for adolescents or secondary and above (≥6 years) for mothers.

## Statistical analysis

We adopted a predefined, a priori approach and a data-driven, a posteriori approach (40, 41) to derive DPs. The a priori approach entailed calculation of individual dietary diversity score (DDS) (33) by summing up the codes received for the 10 food groups (excluding five groups of ultra-processed and deep-fried foods). DDS could range from 0-10. A categorical variable representing quartiles of DDS was created. The a posteriori approach involved cluster analysis (40, 41), given our interest in data-driven grouping of the adolescents into mutually exclusive clusters based on consumption. Cluster analysis assigns participants with similar consumption pattern to the same cluster using Euclidean distance metrics (42). We implemented Simple K-means Clustering algorithm in Weka (maximum 500 iterations) (43) with 14 food groups. The "grains, white roots and tubers, and plantains" group was excluded as 99% of the adolescents consumed rice from that group, making it the least discriminative dietary variable. The algorithm generated cluster centroids representing proportions of consuming adolescents by food groups in that cluster. A line graph was created by plotting the cluster sums of squared errors against cluster numbers ranging from 2-10 (Supplementary Figure 1) to help decide the optimal number of clusters. The point where the slope of the line levels off (the "elbow") indicates the optimal number of clusters. We determined the number of clusters indicating DPs to be four based on the plot, cluster size and interpretability in Matlab context. The clusters were given descriptive labels and a categorical variable was created for further analysis.

Categorical variables are described with frequency and percentage, and continuous variables with mean and standard
<figure><img src="image_11.png" /><figcaption>FIGURE 1
Flowchart for inclusion of Maternal and Infant Nutrition Intervention in Matlab (MINIMat) adolescents into the present study. HH, household.</figcaption></figure>

deviation (SD) or median and interquartile range. We checked the distributions of the continuous variables by examining histograms and quantile-quantile plots. The following right-skewed variables were natural log (Ln) transformed: WC, TG, and HOMA-IR. Means across the clusters and DDS quartiles were compared with one-way analysis of variance (ANOVA). Linear regression models—for all participants and gender-specific—were fitted, and regression coefficients with 95% confidence intervals (CI) are reported. We examined quantile-quantile plots of the residuals and residuals versus fitted plots to rule out violation of assumptions. The adjusted models accounted for gender, household wealth and maternal education based on a directed acyclic graph (Supplementary Figure 2). We did not adjust for BMI considering it a mediator in the DP-cardiometabolic indicator relationship. All tests were two-tailed and *P*-values < 0.05 were considered statistically significant. The analyses were performed in R, version 4.1.2 (44). In this study, a sample size of 2,253 provides 80% power (two-tailed with alpha = 0.05) to detect a difference of 0.06 SD.

## Ethics approval

The 15-year follow-up has been approved by the Ethical Review Committee at icddr,b in Dhaka, Bangladesh (PR-17029; date 2017/05/23). An additional approval has been obtained from the Ethics Review Authority (Etikprövningsmyndigheten) in Sweden (2021-02796; date 2021/11/15). We obtained written informed

consent from the mothers and assent from the adolescents. The study was carried out in accordance with the Declaration of Helsinki.

## Results

Of the 3,267 eligible adolescents, 2,465 completed the household survey. Loss to follow-up resulted from: outmigration (n = 656), child death (n = 94), and refusal to household visit (n = 52). Excluding one participant with missing diet data, the cluster analysis and calculation of DDS involved 2,464 adolescents. The clinic visit was refused by 165 adolescents and another 47 refused venepuncture. Thus, the study sample comprised 2,253 adolescents. Adolescents with missing data on height, and hence, BMI (n = 1); blood pressure (n = 1); TG (n = 8); TC (n = 8); LDL (n = 8); HDL (n = 8); and insulin, and hence, HOMA-IR (n = 8) were excluded from analyses involving the respective variables (Figure 1).

## Description of the diet clusters

The four clusters were labeled as follows: Traditional (n = 832, 33.8%), Fish-dominant (n = 604, 24.5%), Meat-dominant (n = 514, 20.9%), and High-variety (n = 514, 20.9%). The cluster centroids for each food group—which represent the proportions of adolescents who consumed item(s) from that group—are presented in Table 1.
<table><caption>TABLE 1 Proportion of adolescents who consumed at least one tablespoonful (~15 grams) of ≥1 item(s) from the 14 food groups by diet clusters.</caption><thead><tr><th>Food group</th><th>All (n = 2,464)</th><th>Traditional cluster (n = 832)</th><th>Fish-dominant cluster (n = 604)</th><th>Meat-dominant (n = 514)</th><th>High-variety cluster (n = 514)</th></tr></thead><tbody><tr><td>Vitamin A-rich<fnref n="1" /> fruits, vegetables, and tubers</td><td>0.314</td><td>0.252</td><td>0.214</td><td>0.241</td><td>0.605</td></tr><tr><td>Dark green leafy vegetables</td><td>0.268</td><td>0.279</td><td>0.252</td><td>0.214</td><td>0.323</td></tr><tr><td>Other (non-vitamin-A-rich) vegetables</td><td>0.607</td><td>0.739</td><td>0.712</td><td>0.148</td><td>0.731</td></tr><tr><td>Other (non-vitamin-A-rich) fruits</td><td>0.452</td><td>0.296</td><td>0.364</td><td>0.381</td><td>0.877</td></tr><tr><td>Flesh and organ meat</td><td>0.352</td><td>0.139</td><td>0.238</td><td>0.765</td><td>0.418</td></tr><tr><td>Egg</td><td>0.349</td><td>0.279</td><td>0.316</td><td>0.333</td><td>0.517</td></tr><tr><td>Fish</td><td>0.732</td><td>0.907</td><td>0.924</td><td>0.179</td><td>0.774</td></tr><tr><td>Nuts, seeds, and legumes</td><td>0.462</td><td>0.346</td><td>0.290</td><td>0.465</td><td>0.850</td></tr><tr><td>Dairy</td><td>0.305</td><td>0.239</td><td>0.237</td><td>0.249</td><td>0.548</td></tr><tr><td>Ready-to-eat and “instant” foods (UPF)</td><td>0.130</td><td>0.112</td><td>0.134</td><td>0.095</td><td>0.189</td></tr><tr><td>Confectionery, sweets, and similar packaged products (UPF)</td><td>0.534</td><td>0.475</td><td>0.498</td><td>0.451</td><td>0.757</td></tr><tr><td>Savory snacks (UPF)</td><td>0.354</td><td>0.266</td><td>0.379</td><td>0.292</td><td>0.529</td></tr><tr><td>Sugar-sweetened beverage (UPF)</td><td>0.120</td><td>0.055</td><td>0.111</td><td>0.103</td><td>0.251</td></tr><tr><td>Deep-fried foods</td><td>0.413</td><td>0</td><td>1</td><td>0.278</td><td>0.529</td></tr></tbody></table>

UPF, ultra-processed foods.

The Traditional cluster was characterized by a high reported consumption of fish along with low-carotenoid vegetables. About 47.5% of those in this cluster consumed packaged confectionery. The proportions of adolescents consuming animal-source foods (excluding fish) and dark green and vitamin-A-rich vegetables and fruits were fairly low in the cluster. Considering the pattern in Traditional cluster reflective of rural Bangladeshi diets, it was chosen as the reference category in further analyses. The proportion of adolescents consuming fish was highest in the Fish-dominant cluster (92.4%). Consumption of deep-fried foods was universal in this cluster and nearly 50% of the adolescents also consumed packaged confectionery. The Meat-dominant cluster was distinguished by the highest proportion of adolescents consuming meat (76.5%). Consumption of legumes (46.5%) and packaged confectionery (45.1%) was high as well, whereas that of fish was markedly low (17.9%). The proportion of adolescents with consumption exceeded 50% for 10 out of the 14 food groups in the High-variety cluster. Three-fourths of the adolescents in this cluster consumed packaged confectionery and about 53% consumed savory snacks and deep-fried foods. Consumption of low-carotenoid fruits, nuts, seeds, and legumes was also high.

The mean DDS across the clusters were as follows: Traditional (4.4; 95% CI: 4.3–4.5), Fish-dominant (4.5; 95% CI: 4.4–4.6), Meat-dominant (3.9; 95% CI: 3.8–4.0), and High-variety (6.6; 95% CI: 6.5–6.7). The mean BAZ of the adolescents in these clusters were as follows: Traditional (−0.89; 95% CI: −0.98, −0.80), Fish-dominant (−0.91; 95% CI: −1.01, −0.81), Meat-dominant (−1.01; 95% CI: −1.12, −0.89), and High-variety (−0.96; 95% CI: −1.08, −0.84).

## Characteristics of the study participants

Table 2 demonstrates the socio-demographic and anthropometric characteristics and cardiometabolic profile of the MINIMat adolescents. The girls were slightly over-represented

in the sample (52.1%). On average, the boys were taller and heavier than the girls; but the median BMI was higher among the girls. Overweight/obesity was more prevalent among the girls than the boys (7.9 versus 6.2%). Approximately 40% of the girls belonged to the poorest households, whereas 28.5% of the boys came from the poorest households. There were more girls than boys in the Traditional cluster and in the first quartile of DDS. The median WC in the sample was 62.4 cm and it did not differ by gender.

## Bivariate analysis

The mean levels of WC, SBP, and blood markers by cluster and DDS quartile are presented in Table 3. There was a small but statistically significant difference in TG level by cluster. Adolescents in the Traditional cluster had the highest mean TG (geometric mean 0.99 mmol/L, 95% CI: 0.96–1.02), while those in the Fish-dominant cluster had the lowest mean TG (geometric mean 0.92 mmol/L, 95% CI: 0.89–0.95). Small but statistically significant differences in means of WC, SBP, and HDL were observed across DDS quartiles.

## Associations of diet clusters with cardiometabolic risk indicators

Table 4 shows the regression coefficients with 95% CIs from crude and adjusted linear models for associations between the clusters and cardiometabolic risk indicators. Overall, the diet clusters were not associated with the indicators. On gender stratification, TG levels were significantly lower among boys in the Fish-dominant cluster (Ln TG β<sub>adj</sub>: −0.089; 95% CI: −0.155, −0.022) and Meat-dominant cluster (Ln TG β<sub>adj</sub>: −0.076; 95% CI: −0.149, −0.004) compared to boys in the Traditional cluster. No statistically significant association was observed among the girls.

<footnote n="1">¹Based on the FAO guideline (35) that defines Vitamin A-rich fruits, vegetables, and tubers as those containing at least 120 Retinol Equivalent per 100 gram.</footnote>
<table><caption>TABLE 2 Descriptive characteristics of the adolescents participating in the study.</caption><thead><tr><th rowspan="2">Characteristics</th><th colspan="2">All (n = 2253)</th><th colspan="2">Boys (n = 1079)</th><th colspan="2">Girls (n = 1174)</th><th rowspan="2">p<sup>1</sup></th></tr><tr><th>n</th><th>Value</th><th>n</th><th>Value</th><th>n</th><th>Value</th></tr></thead><tbody><tr><td>Age (years)</td><td>2,253</td><td>15.0 (0.1)</td><td>1,079</td><td>15.0 (0.1)</td><td>1,174</td><td>15.0 (0.1)</td><td>0.382</td></tr><tr><td>Height (cm)</td><td>2,251</td><td>156.5 (7.6)</td><td>1,078</td><td>160.5 (7.7)</td><td>1,173</td><td>152.9 (5.4)</td><td><strong>&lt;0.001</strong></td></tr><tr><td>Weight (kg)</td><td>2,253</td><td>43.9 (39.3-49.7)</td><td>1,079</td><td>45.3 (39.3-51.0)</td><td>1,174</td><td>43.1 (39.2-48.5)</td><td><strong>&lt;0.001</strong></td></tr><tr><td>BMI (kg/m<sup>2</sup>)</td><td>2,251</td><td>17.8 (16.3-19.8)</td><td>1,078</td><td>17.2 (15.9-18.9)</td><td>1,173</td><td>18.4 (16.9-20.5)</td><td><strong>&lt;0.001</strong></td></tr><tr><td>BAZ categories<sup>2</sup></td><td>2,251</td><td></td><td></td><td></td><td></td><td></td><td><strong>&lt;0.001</strong></td></tr><tr><td>Normal</td><td>1,649</td><td>73.3</td><td>707</td><td>65.6</td><td>942</td><td>80.3</td><td></td></tr><tr><td>Thin</td><td>442</td><td>19.6</td><td>304</td><td>28.2</td><td>138</td><td>11.8</td><td></td></tr><tr><td>Overweight/obese</td><td>160</td><td>7.1</td><td>67</td><td>6.2</td><td>93</td><td>7.9</td><td></td></tr><tr><td>Household wealth</td><td>2,253</td><td></td><td></td><td></td><td></td><td></td><td><strong>&lt;0.001</strong></td></tr><tr><td>Poorest</td><td>775</td><td>34.4</td><td>308</td><td>28.5</td><td>467</td><td>39.8</td><td></td></tr><tr><td>Intermediate</td><td>723</td><td>32.1</td><td>397</td><td>36.8</td><td>326</td><td>27.8</td><td></td></tr><tr><td>Richest</td><td>755</td><td>33.5</td><td>374</td><td>34.7</td><td>381</td><td>32.4</td><td></td></tr><tr><td>Maternal education</td><td>2,253</td><td></td><td></td><td></td><td></td><td></td><td><strong>0.027</strong></td></tr><tr><td>None</td><td>447</td><td>19.8</td><td>210</td><td>19.5</td><td>237</td><td>20.2</td><td></td></tr><tr><td>Primary</td><td>800</td><td>35.5</td><td>357</td><td>33.1</td><td>443</td><td>37.7</td><td></td></tr><tr><td>Secondary or above</td><td>1,006</td><td>44.7</td><td>512</td><td>47.4</td><td>494</td><td>42.1</td><td></td></tr><tr><td>Adolescent education</td><td></td><td></td><td></td><td></td><td></td><td></td><td><strong>&lt;0.001</strong></td></tr><tr><td>None</td><td>355</td><td>15.8</td><td>214</td><td>19.8</td><td>141</td><td>12.0</td><td></td></tr><tr><td>Primary</td><td>57</td><td>2.5</td><td>45</td><td>4.2</td><td>12</td><td>1.0</td><td></td></tr><tr><td>Secondary</td><td>1,841</td><td>81.7</td><td>820</td><td>76.0</td><td>1,021</td><td>87.0</td><td></td></tr><tr><td>Diet clusters</td><td>2,253</td><td></td><td></td><td></td><td></td><td></td><td><strong>&lt;0.001</strong></td></tr><tr><td>Traditional</td><td>764</td><td>33.9</td><td>282</td><td>26.1</td><td>482</td><td>41.0</td><td></td></tr><tr><td>Fish-dominant</td><td>559</td><td>24.8</td><td>316</td><td>29.3</td><td>243</td><td>20.7</td><td></td></tr><tr><td>Meat-dominant</td><td>468</td><td>20.8</td><td>222</td><td>20.6</td><td>246</td><td>21.0</td><td></td></tr><tr><td>High-variety</td><td>462</td><td>20.5</td><td>259</td><td>24.0</td><td>203</td><td>17.3</td><td></td></tr><tr><td>DDS quartiles</td><td>2,253</td><td></td><td></td><td></td><td></td><td></td><td><strong>0.026</strong></td></tr><tr><td>Q1 (DDS ≤ 4)</td><td>965</td><td>42.8</td><td>439</td><td>40.7</td><td>526</td><td>44.8</td><td></td></tr><tr><td>Q2 (DDS = 5)</td><td>568</td><td>25.2</td><td>262</td><td>24.3</td><td>306</td><td>26.1</td><td></td></tr><tr><td>Q3 (DDS = 6)</td><td>427</td><td>19.0</td><td>227</td><td>21.0</td><td>200</td><td>17.0</td><td></td></tr><tr><td>Q4 (DDS ≥ 7)</td><td>293</td><td>13.0</td><td>151</td><td>14.0</td><td>142</td><td>12.1</td><td></td></tr><tr><td>DDS</td><td>2,253</td><td>4.8 (1.5)</td><td>1,079</td><td>4.9 (1.6)</td><td>1,174</td><td>4.7 (1.4)</td><td></td></tr><tr><td>WC (cm)</td><td>2,253</td><td>62.4 (58.9-66.8)</td><td>1,079</td><td>62.4 (59.3-66.4)</td><td>1,174</td><td>62.4 (58.8-67.2)</td><td>0.989</td></tr><tr><td>SBP (mm of Hg)</td><td>2,252</td><td>108.8 (8.0)</td><td>1,078</td><td>110.3 (8.2)</td><td>1,174</td><td>107.4 (7.6)</td><td><strong>&lt;0.001</strong></td></tr><tr><td>TG (mmol/L)</td><td>2,245</td><td>0.9 (0.7-1.2)</td><td>1,077</td><td>0.9 (0.7-1.1)</td><td>1,168</td><td>1.0 (0.8-1.3)</td><td><strong>&lt;0.001</strong></td></tr><tr><td>TC (mmol/L)</td><td>2,245</td><td>3.6 (0.7)</td><td>1,077</td><td>3.4 (0.6)</td><td>1,168</td><td>3.7 (0.7)</td><td><strong>&lt;0.001</strong></td></tr><tr><td>LDL (mmol/L)</td><td>2,245</td><td>2.1 (0.6)</td><td>1,077</td><td>2.0 (0.5)</td><td>1,168</td><td>2.2 (0.6)</td><td><strong>&lt;0.001</strong></td></tr><tr><td>HDL (mmol/L)</td><td>2,245</td><td>1.0 (0.2)</td><td>1,077</td><td>1.0 (0.2)</td><td>1,168</td><td>1.0 (0.2)</td><td>0.441</td></tr><tr><td>Insulin (μU/L)</td><td>2,245</td><td>11.4 (7.8-18.3)</td><td>1,078</td><td>9.6 (6.6-16.3)</td><td>1,167</td><td>12.9 (9.3-20.2)</td><td><strong>&lt;0.001</strong></td></tr><tr><td>Glucose (mmol/L)</td><td>2,253</td><td>5.2 (0.5)</td><td>1,079</td><td>5.3 (0.5)</td><td>1,174</td><td>5.1 (0.4)</td><td><strong>&lt;0.001</strong></td></tr><tr><td>HOMA-IR</td><td>2,245</td><td>2.6 (1.7-4.2)</td><td>1,078</td><td>2.2 (1.5-3.8)</td><td>1,167</td><td>2.9 (2.1-4.7)</td><td><strong>&lt;0.001</strong></td></tr></tbody></table>

BMI, body mass index; BAZ, BMI-for-age z-score; WC, waist circumference; DDS, dietary diversity score; Q, quartile; SBP, systolic blood pressure; TG, triglyceride; TC, total cholesterol; LDL, low-density lipoprotein; HDL, high-density lipoprotein; HOMA-IR, insulin resistance from Homeostasis Model Assessment.

Values represent percentage for categorical variables, mean with standard deviation for continuous variables (approximately) normally distributed, or median with inter-quartile range for continuous variables that were skewed. P-values in bold indicate statistical significance.

Missing data: height and BMI (n = 2), SBP (n = 1), TG (n = 8), TC (n = 8), LDL (n = 8), HDL (n = 8), and insulin and HOMA-IR (n = 8).

¹P-value for gender difference from Chi-squared test, independent samples t-test, or Wilcoxon rank-sum test.

²BAZ below -2 SD and above + 1 SD define thinness and overweight/obesity, respectively.
<table><caption>TABLE 3 Levels of cardiometabolic risk markers by diet clusters and quartiles of dietary diversity score.</caption><thead><tr><th>Diet clusters</th><th>WC<sup>1</sup> (cm)</th><th>SBP (mm of Hg)</th><th>TG<sup>1</sup> (mmol/L)</th><th>TC (mmol/L)</th><th>LDL (mmol/L)</th><th>HDL (mmol/L)</th><th>HOMA-IR<sup>1</sup></th></tr></thead><tbody><tr><td colspan="8">Mean (95% confidence interval)</td></tr><tr><td>Traditional</td><td>63.3 (62.8-63.8)</td><td>108.5 (107.9-109.0)</td><td>0.99 (0.96-1.02)</td><td>3.62 (3.57-3.67)</td><td>2.16 (2.12-2.20)</td><td>1.02 (1.004-1.04)</td><td>3.1 (2.9-3.2)</td></tr><tr><td>Fish-dominant</td><td>63.6 (63.0-64.2)</td><td>109.0 (108.3-109.6)</td><td>0.92 (0.89-0.95)</td><td>3.54 (3.49-3.59)</td><td>2.12 (2.07-2.16)</td><td>1.02 (1.004-1.04)</td><td>2.8 (2.6-2.9)</td></tr><tr><td>Meat-dominant</td><td>63.0 (62.4-63.6)</td><td>108.4 (107.7-109.2)</td><td>0.94 (0.91-0.98)</td><td>3.54 (3.48-3.60)</td><td>2.11 (2.06-2.16)</td><td>1.01 (0.99-1.03)</td><td>2.8 (2.6-3.0)</td></tr><tr><td>High-variety</td><td>63.5 (62.9-64.1)</td><td>109.5 (108.8-110.3)</td><td>0.93 (0.89-0.96)</td><td>3.55 (3.48-3.61)</td><td>2.12 (2.07-2.18)</td><td>1.01 (0.99-1.03)</td><td>3.0 (2.8-3.2)</td></tr><tr><td>p<sup>2</sup></td><td colspan="2">0.524</td><td>0.115</td><td><strong>0.003</strong></td><td>0.086</td><td>0.424</td><td>0.779</td><td>0.070</td></tr><tr><td colspan="8">DDS quartiles</td></tr><tr><td>Q1 (DDS ≤ 4)</td><td>63.2 (62.7-63.6)</td><td>108.2 (107.7-108.8)</td><td>0.96 (0.93-0.98)</td><td>3.57 (3.53-3.61)</td><td>2.13 (2.09-2.17)</td><td>1.01 (1.001-1.03)</td><td>2.8 (2.7-3.0)</td></tr><tr><td>Q2 (DDS = 5)</td><td>63.5 (63.0-64.1)</td><td>109.2 (108.6-109.9)</td><td>0.97 (0.94-1.00)</td><td>3.57 (3.52-3.62)</td><td>2.13 (2.08-2.17)</td><td>1.01 (0.99-1.03)</td><td>3.1 (2.9-3.3)</td></tr><tr><td>Q3 (DDS = 6)</td><td>62.9 (62.3-63.5)</td><td>108.7 (108.0-109.4)</td><td>0.92 (0.89-0.96)</td><td>3.57 (3.50-3.64)</td><td>2.12 (2.06-2.18)</td><td>1.04 (1.02-1.07)</td><td>2.9 (2.7-3.1)</td></tr><tr><td>Q4 (DDS ≥ 7)</td><td>64.3 (63.5-65.1)</td><td>110.0 (109.1-111.0)</td><td>0.94 (0.89-0.98)</td><td>3.56 (3.48-3.64)</td><td>2.14 (2.07-2.21)</td><td>0.99 (0.97-1.02)</td><td>3.0 (2.7-3.2)</td></tr><tr><td>p<sup>2</sup></td><td colspan="2"><strong>0.03</strong></td><td><strong>0.005</strong></td><td>0.275</td><td>0.992</td><td>0.962</td><td><strong>0.017</strong></td><td>0.076</td></tr></tbody></table>

WC, waist circumference; SBP, systolic blood pressure; TG, triglyceride; TC, total cholesterol; LDL, low-density lipoprotein; HDL, high-density lipoprotein; HOMA-IR, insulin resistance from Homeostasis Model Assessment; DDS, dietary diversity score; Q, quartile. P-values in bold indicate statistical significance.

## Associations of DDS quartiles with cardiometabolic risk indicators

The adjusted linear regression models analyzing associations between DDS quartiles and cardiometabolic risk indicators are presented in Table 5. On average, those in the second (DDS = 5) and top (DDS ≥ 7) quartiles had 0.9 (95% CI: 0.1–1.7) and 1.4 (95% CI: 0.4–2.4) mm of Hg higher SBP, respectively, than their peers in the bottom quartile (DDS ≤ 4). Although on gender stratification the association disappeared among the girls, it persisted among the boys ($\beta_{adj}$ for boys in the top quartile: 2.1; 95% CI: 0.5–3.6). Compared to the boys in the bottom quartile, WC was 1.9% (95% CI: 0.01–3.8%) higher among the boys in the top quartile. Boys in the third quartile (DDS = 6) had 0.04 mmol/L (95% CI: 0.01–0.08) higher HDL than the boys in the bottom quartile. These associations were not observed among the girls.

## Discussion

We explored cross-sectional associations of DPs derived from cluster analysis and DDS with conventional cardiometabolic risk indicators in a rural birth cohort. Four distinct clusters were identified: Traditional, Fish-dominant, Meat-dominant, and High-variety; and none was associated with the indicators overall. However, gender-stratified analyses revealed lower plasma TG levels among boys in the Fish- and Meat-dominant clusters than boys in the Traditional cluster. The associations of DDS quartiles with WC, SBP, and HDL were significant only among the boys as well. Compared to boys in the bottom quartile of DDS, higher WC, and SBP were observed among boys in the top quartile.

The diet clusters identified in our study aligned with those found among 9–15 years old adolescents (n = 30,702, mean age 11.6 years, 49.6% girls) in rural, northwest Bangladesh between 2015 and 2017 (45). The researchers identified five *a posteriori* DPs using latent class analysis of dietary data collected through a food frequency questionnaire in that study. Despite the methodological

differences, the “traditional”, “moderately high meat”, and “most diverse” DPs identified in that study (45) resemble the Traditional, Meat-dominant, and High-variety DPs from the present study, respectively. High consumption of fish and low-carotenoid vegetables along with low consumption of animal-source foods including dairy characterized the DP considered traditional in both analyses. Moreover, a DP with higher diversity from consumption of nutrient-rich foods (e.g., vitamin-A-rich fruits and vegetables, animal-source foods and legumes) alongside unhealthy ultra-processed and deep-fried foods emerged in both analyses. Adolescents from relatively well-off households tended to have this DP in both studies. This potentially indicates a socio-economic gradient in rural Bangladesh (46) that allows consumption of a combination of nutrient-rich and empty-calorie foods among adolescents of relatively affluent families. Interestingly, the mean BAZ was about 0.1 SD higher among those with the diverse DP (“most diverse” and High-variety) than those with the traditional DP in both studies.

The *a posteriori* DPs did not show an overall association with the cardiometabolic risk indicators in this study. Several previous studies conducted among adolescents that employed cluster analysis (10, 13, 14, 47) also reported a general lack of association. This can be related to the variation in the relationship between diet and cardiometabolic risk across life phases. It has been shown that short-term associations between DPs and cardiometabolic risk indicators are attenuated among adolescents compared to adults in their fourth to sixth decades of life (48). Adolescents usually remain metabolically healthy around mid-adolescence, even including a proportion of those with obesity (49), and the impact of diet on specific cardiometabolic indicators may require a longer period to manifest (48). Moreover, the dietary share of foods that are major sources of saturated and trans fats, such as red and processed meat, full-fat dairy products and ready-to-eat/heat UPFs (50), were probably much lower in the rural setting of Matlab than in high-income settings. These foods contribute to more than 50% of the daily energy intake among adolescents in some high-income countries (51–53). Alternatively, DP-indicator associations might have been masked by differences in portion size and preparation style (e.g., deep versus shallow frying),

<footnote n="1">¹WC, TG, and HOMA-IR were right skewed and (natural) log transformed, and their geometric means with 95% confidence intervals reported here.</footnote>

<footnote n="2">²From one-way analysis of variance.</footnote>
<table><caption>TABLE 4 Overall and gender-stratified associations of diet clusters with anthropometric and blood markers of cardiometabolic risk among the Maternal and Infant Nutrition Intervention in Matlab (MINIMat) adolescents.</caption><thead><tr><th rowspan="2">Marker</th><th rowspan="2">Diet clusters</th><th colspan="2">All (N = 2,253)</th><th colspan="2">Boys (N = 1,079)</th><th colspan="2">Girls (N = 1,174)</th></tr><tr><th>β (95% CI)</th><th>P</th><th>β (95% CI)</th><th>P</th><th>β (95% CI)</th><th>P</th></tr></thead><tbody><tr><td rowspan="3">Ln WC Crude model</td><td>Traditional</td><td>Ref</td><td></td><td>Ref</td><td></td><td>Ref</td><td></td></tr><tr><td>Fish-dominant</td><td>0.005 (-0.007, 0.016)</td><td>0.435</td><td>0.007 (-0.009, 0.024)</td><td>0.376</td><td>0.003 (-0.014, 0.019)</td><td>0.766</td></tr><tr><td>Meat-dominant</td><td>-0.005 (-0.017, 0.007)</td><td>0.434</td><td>-0.003 (-0.021, 0.015)</td><td>0.720</td><td>-0.006 (-0.022, 0.011)</td><td>0.517</td></tr><tr><td></td><td>High-variety</td><td>0.003 (-0.010, 0.015)</td><td>0.674</td><td>0.007 (-0.010, 0.025)</td><td>0.402</td><td>-0.002 (-0.020, 0.016)</td><td>0.835</td></tr><tr><td rowspan="3">Ln WC Adjusted<sup>1</sup> model</td><td>Traditional</td><td>Ref</td><td></td><td>Ref</td><td></td><td>Ref</td><td></td></tr><tr><td>Fish-dominant</td><td>0.002 (-0.009, 0.013)</td><td>0.724</td><td>0.009 (-0.007, 0.025)</td><td>0.281</td><td>-0.003 (-0.019, 0.014)</td><td>0.739</td></tr><tr><td>Meat-dominant</td><td>-0.007 (-0.019, 0.004)</td><td>0.221</td><td>-0.004 (-0.021, 0.014)</td><td>0.669</td><td>-0.009 (-0.025, 0.007)</td><td>0.284</td></tr><tr><td></td><td>High-variety</td><td>-0.004 (-0.016, 0.009)</td><td>0.567</td><td>0.002 (-0.015, 0.019)</td><td>0.840</td><td>-0.008 (-0.026, 0.009)</td><td>0.356</td></tr><tr><td rowspan="3">SBP Crude model</td><td>Traditional</td><td>Ref</td><td></td><td>Ref</td><td></td><td>Ref</td><td></td></tr><tr><td>Fish-dominant</td><td>0.473 (-0.403, 1.348)</td><td>0.290</td><td>-0.007 (-1.332, 1.317)</td><td>0.992</td><td>-0.120 (-1.290, 1.049)</td><td>0.840</td></tr><tr><td>Meat-dominant</td><td>-0.051 (-0.975, 0.873)</td><td>0.913</td><td>-0.221 (-1.674, 1.231)</td><td>0.765</td><td>-0.432 (-1.596, 0.733)</td><td>0.467</td></tr><tr><td></td><td>High-variety</td><td><strong>1.049 (0.121, 1.976)</strong></td><td><strong>0.027</strong></td><td>0.815 (-0.577, 2.206)</td><td>0.251</td><td>0.171 (-1.072, 1.415)</td><td>0.787</td></tr><tr><td rowspan="3">SBP Adjusted<sup>1</sup> model</td><td>Traditional</td><td>Ref</td><td></td><td>Ref</td><td></td><td>Ref</td><td></td></tr><tr><td>Fish-dominant</td><td>-0.130 (-1.002, 0.742)</td><td>0.770</td><td>0.047 (-1.275, 1.369)</td><td>0.944</td><td>-0.169 (-1.346, 1.008)</td><td>0.778</td></tr><tr><td>Meat-dominant</td><td>-0.405 (-1.319, 0.508)</td><td>0.384</td><td>-0.263 (-1.713, 1.188)</td><td>0.722</td><td>-0.443 (-1.614, 0.728)</td><td>0.458</td></tr><tr><td></td><td>High-variety</td><td>0.356 (-0.570, 1.281)</td><td>0.451</td><td>0.602 (-0.796, 2.001)</td><td>0.398</td><td>0.098 (-1.155, 1.350)</td><td>0.878</td></tr><tr><td rowspan="3">Ln TG Crude model</td><td>Traditional</td><td>Ref</td><td></td><td>Ref</td><td></td><td>Ref</td><td></td></tr><tr><td>Fish-dominant</td><td>-0.076 (-0.121, -0.032)</td><td>&lt;0.001</td><td><strong>-0.089 (-0.155, -0.023)</strong></td><td><strong>0.008</strong></td><td>-0.007 (-0.069, 0.054)</td><td>0.814</td></tr><tr><td>Meat-dominant</td><td>-0.049 (-0.096, -0.002)</td><td>0.041</td><td><strong>-0.076 (-0.148, -0.004)</strong></td><td><strong>0.039</strong></td><td>-0.003 (-0.063, 0.058)</td><td>0.932</td></tr><tr><td></td><td>High-variety</td><td>-0.067 (-0.114, -0.020)</td><td>0.005</td><td>-0.052 (-0.121, 0.017)</td><td>0.140</td><td>-0.038 (-0.102, 0.027)</td><td>0.257</td></tr><tr><td rowspan="3">Ln TG Adjusted<sup>1</sup> model</td><td>Traditional</td><td>Ref</td><td></td><td>Ref</td><td></td><td>Ref</td><td></td></tr><tr><td>Fish-dominant</td><td>-0.045 (-0.089, 0.00007)</td><td>0.050</td><td><strong>-0.089 (-0.155, -0.022)</strong></td><td><strong>0.009</strong></td><td>0.001 (-0.060, 0.062)</td><td>0.974</td></tr><tr><td>Meat-dominant</td><td>-0.031 (-0.077, 0.016)</td><td>0.196</td><td><strong>-0.076 (-0.149, -0.004)</strong></td><td><strong>0.039</strong></td><td>0.006 (-0.055, 0.067)</td><td>0.846</td></tr><tr><td></td><td>High-variety</td><td>-0.032 (-0.079, 0.015)</td><td>0.181</td><td>-0.053 (-0.122, 0.017)</td><td>0.140</td><td>-0.026 (-0.091, -0.032)</td><td>0.439</td></tr><tr><td rowspan="3">TC Crude model</td><td>Traditional</td><td>Ref</td><td></td><td>Ref</td><td></td><td>Ref</td><td></td></tr><tr><td>Fish-dominant</td><td><strong>-0.078 (-0.151, -0.005)</strong></td><td><strong>0.037</strong></td><td>-0.025 (-0.121, 0.071)</td><td>0.614</td><td>-0.005 (-0.113, 0.103)</td><td>0.924</td></tr><tr><td>Meat-dominant</td><td><strong>-0.081 (-0.158, -0.003)</strong></td><td><strong>0.040</strong></td><td>-0.064 (-0.170, 0.041)</td><td>0.229</td><td>-0.034 (-0.142, 0.073)</td><td>0.527</td></tr><tr><td></td><td>High-variety</td><td>-0.070 (-0.147, 0.007)</td><td>0.077</td><td>-0.026 (-0.127, 0.074)</td><td>0.606</td><td>0.009 (-0.105, 0.124)</td><td>0.875</td></tr><tr><td rowspan="3">TC Adjusted<sup>1</sup> model</td><td>Traditional</td><td>Ref</td><td></td><td>Ref</td><td></td><td>Ref</td><td></td></tr><tr><td>Fish-dominant</td><td>-0.019 (-0.091, 0.053)</td><td>0.610</td><td>-0.024 (-0.120, 0.071)</td><td>0.615</td><td>-0.009 (-0.118, 0.010)</td><td>0.864</td></tr><tr><td>Meat-dominant</td><td>-0.044 (-0.120, 0.031)</td><td>0.248</td><td>-0.060 (-0.165, 0.045)</td><td>0.262</td><td>-0.029 (-0.136, 0.079)</td><td>0.600</td></tr><tr><td></td><td>High-variety</td><td>-0.013 (-0.089, 0.064)</td><td>0.746</td><td>-0.032 (-0.133, 0.070)</td><td>0.539</td><td>0.006 (-0.109, 0.121)</td><td>0.921</td></tr><tr><td rowspan="3">LDL Crude model</td><td>Traditional</td><td>Ref</td><td></td><td>Ref</td><td></td><td>Ref</td><td></td></tr><tr><td>Fish-dominant</td><td>-0.041 (-0.103, 0.022)</td><td>0.202</td><td>0.007 (-0.073, 0.088)</td><td>0.855</td><td>0.016 (-0.078, 0.110)</td><td>0.743</td></tr><tr><td>Meat-dominant</td><td>-0.049 (-0.115, 0.017)</td><td>0.146</td><td>-0.025 (-0.113, 0.063)</td><td>0.579</td><td>-0.019 (-0.112, 0.074)</td><td>0.685</td></tr><tr><td></td><td>High-variety</td><td>-0.035 (-0.101, 0.031)</td><td>0.296</td><td>0.008 (-0.076, 0.092)</td><td>0.850</td><td>0.022 (-0.077, 0.122)</td><td>0.657</td></tr><tr><td rowspan="3">LDL Adjusted<sup>1</sup> model</td><td>Traditional</td><td>Ref</td><td></td><td>Ref</td><td></td><td>Ref</td><td></td></tr><tr><td>Fish-dominant</td><td>0.006 (-0.056, 0.067)</td><td>0.860</td><td>0.008 (-0.072, 0.087)</td><td>0.853</td><td>0.009 (-0.085, 0.103)</td><td>0.854</td></tr><tr><td>Meat-dominant</td><td>-0.021 (-0.085, 0.044)</td><td>0.531</td><td>-0.021 (-0.109, 0.066)</td><td>0.631</td><td>-0.017 (-0.110, 0.077)</td><td>0.728</td></tr><tr><td></td><td>High-variety</td><td>0.007 (-0.058, 0.072)</td><td>0.844</td><td>-0.003 (-0.087, 0.082)</td><td>0.952</td><td>0.016 (-0.084, 0.115)</td><td>0.759</td></tr><tr><td rowspan="2">HDL Crude model</td><td>Traditional</td><td>Ref</td><td></td><td>Ref</td><td></td><td>Ref</td><td></td></tr><tr><td>Fish-dominant</td><td>0.002 (-0.022, 0.026)</td><td>0.867</td><td>0.003 (-0.033, 0.039)</td><td>0.876</td><td>-0.005 (-0.040, 0.029)</td><td>0.761</td></tr></tbody></table>

(Continued)
<table><caption>TABLE 4 (Continued)</caption><thead><tr><th rowspan="2">Marker</th><th rowspan="2">Diet clusters</th><th colspan="2">All (N = 2,253)</th><th colspan="2">Boys (N = 1,079)</th><th colspan="2">Girls (N = 1,174)</th></tr><tr><th>β (95% CI)</th><th>P</th><th>β (95% CI)</th><th>P</th><th>β (95% CI)</th><th>P</th></tr></thead><tbody><tr><td rowspan="2"></td><td>Meat-dominant</td><td>-0.011 (-0.036, 0.015)</td><td>0.402</td><td>-0.016 (-0.055, 0.023)</td><td>0.429</td><td>-0.009 (-0.043, 0.025)</td><td>0.593</td></tr><tr><td>High-variety</td><td>-0.006 (-0.031, 0.020)</td><td>0.664</td><td>-0.023 (-0.061, 0.014)</td><td>0.228</td><td>0.010 (-0.026, 0.047)</td><td>0.573</td></tr><tr><td rowspan="4">HDL Adjusted<sup>1</sup> model</td><td>Traditional</td><td>Ref</td><td></td><td>Ref</td><td></td><td>Ref</td><td></td></tr><tr><td>Fish-dominant</td><td>-0.00001 (-0.025, 0.025)</td><td>0.998</td><td>0.002 (-0.034, 0.038)</td><td>0.913</td><td>-0.007 (-0.042, 0.028)</td><td>0.691</td></tr><tr><td>Meat-dominant</td><td>-0.011 (-0.036, 0.015)</td><td>0.418</td><td>-0.013 (-0.053, 0.026)</td><td>0.498</td><td>-0.010 (-0.044, 0.024)</td><td>0.558</td></tr><tr><td>High-variety</td><td>-0.006 (-0.032, 0.020)</td><td>0.646</td><td>-0.016 (-0.054, 0.022)</td><td>0.408</td><td>0.008 (-0.029, 0.045)</td><td>0.673</td></tr><tr><td rowspan="4">Ln HOMA-IR Crude model</td><td>Traditional</td><td>Ref</td><td></td><td>Ref</td><td></td><td>Ref</td><td></td></tr><tr><td>Fish-dominant</td><td><b>-0.103 (-0.188, -0.018)</b></td><td><b>0.017</b></td><td>-0.092 (-0.222, 0.037)</td><td>0.163</td><td>-0.008 (-0.121, 0.104)</td><td>0.884</td></tr><tr><td>Meat-dominant</td><td>-0.087 (-0.176, 0.002)</td><td>0.057</td><td>-0.072 (-0.214, 0.070)</td><td>0.322</td><td>-0.054 (-0.165, 0.057)</td><td>0.342</td></tr><tr><td>High-variety</td><td>-0.033 (-0.122, 0.057)</td><td>0.476</td><td>0.019 (-0.117, 0.156)</td><td>0.781</td><td>0.003 (-0.116, 0.122)</td><td>0.958</td></tr><tr><td rowspan="4">Ln HOMA-IR Adjusted<sup>2</sup> model</td><td>Traditional</td><td>Ref</td><td></td><td>Ref</td><td></td><td>Ref</td><td></td></tr><tr><td>Fish-dominant</td><td>-0.055 (-0.138, 0.027)</td><td>0.189</td><td>-0.090 (-0.218, 0.038)</td><td>0.170</td><td>-0.026 (-0.135, 0.082)</td><td>0.634</td></tr><tr><td>Meat-dominant</td><td>-0.052 (-0.139, 0.034)</td><td>0.236</td><td>-0.068 (-0.209, 0.072)</td><td>0.340</td><td>-0.040 (-0.148, 0.068)</td><td>0.464</td></tr><tr><td>High-variety</td><td>-0.028 (-0.116, 0.059)</td><td>0.527</td><td>-0.016 (-0.152, 0.120)</td><td>0.814</td><td>-0.043 (-0.158, 0.072)</td><td>0.464</td></tr></tbody></table>

Ref, reference category; Ln, natural-log transformed variable where the base of the log was 2.71828; b, regression coefficient; WC, waist circumference; SBP, systolic blood pressure; TG, triglyceride; TC, total cholesterol; LDL, low-density lipoprotein; HDL, high-density lipoprotein; HOMA-IR, insulin resistance from Homeostasis Model Assessment.

and thus, in energy intake, as we did not quantify and adjust for total energy intake. Nevertheless, precise assessment of portion size is challenging in a rural setting where family members share food from a common bowl (54).

We found gender-specific negative associations of Fish- and Meat-dominant DPs with plasma TG. These translated into 8.5% lower TG among boys in the Fish-dominant cluster and 7.3% lower TG among boys in the Meat-dominant cluster than among boys in the Traditional cluster. The mechanism underlying this gender specificity remains unclear. While statistically significant, the “effect size” in terms of differences in TG level appeared small from a public health perspective (55). Adherence to a Mediterranean DP was inversely associated with serum TG among adolescent boys from Mexico City (19). Conversely, a “Rice and Kimchi” DP among South Korean adolescents with about 45% of the energy intake from white rice was associated with an elevated TG in both gender (14). The traditional DP in rural Bangladesh is dominated by white rice-a starchy staple with high glycemic index (GI) (56)-that contributes to up to 76% of the total energy intake among adolescents (57). Diets dominated by white rice have been linked to raised TG levels (58). It has been postulated that substantial intake of high-GI foods combined with relatively low intake of cholesterol and saturated fat, that is more likely in the Traditional cluster, may stimulate production of triglycerides (59). The negative associations with TG could be related to a replacement of white rice to some extent by protein intake from fish and meat in the Fish- and Meat-dominant clusters (60). Nonetheless, the qualitative recall precluded quantification of the dietary share of white rice across the clusters and further studies are needed to examine this assumption.

Some unanticipated associations emerged in relation to *a priori* DPs based on the quartiles of DDS. Having a DDS ≥ 7 was associated with higher SBP and the association was slightly amplified among

the boys on gender stratification. A recent meta-analysis of studies among adults could not find any significant association between DDS and systolic or diastolic blood pressure (61). However, studies among adolescents involving *a priori* DPs generated conflicting results. Truthmann and colleagues (12) documented a positive association between dietary diversity and SBP among girls in a cross-sectional study (n = 5,198, mean age 15.1 years, 49.1% girls) from Germany. In contrast, DASH (Dietary Approaches to Stop Hypertension) scores showed no association with SBP in several studies (10, 62, 63). The instruments and the number and composition of food groups used to construct the *a priori* scores varied substantially between these studies, and limited the comparability. The positive associations with SBP and WC in our study may imply a greater intake of added salt and unhealthy fats from ultra-processed and deep-fried foods among the boys in the top DDS quartile. Among the boys in the top quartile (n = 151), the proportions of those who consumed these foods were high: 22.5% for ready-to-eat foods, 72.2% for confectioneries, 51.6% for savory snacks, 29.1% for SSB, and 56.9% for deep-fried foods (data not shown). Adjusting for total energy intake might have attenuated these associations. For instance, in a cross-sectional study among Iranian adolescents (n = 456, mean age 14 years, 58.5% girls), introduction of total energy intake in the regression model rendered the positive association between DDS and abdominal obesity (WC ≥ 85th percentile) statistically non-significant (17). The reason for the isolated association of having a DDS = 6 with higher HDL remains inconspicuous. The simple method used for calculating DDS in this study (33) satisfactorily predicted micronutrient adequacy among Bangladeshi adolescents (32). Nevertheless, it perhaps lacks the nuance and sensitivity required to capture associations between diet and cardiometabolic risk at mid-adolescence (61).

<footnote n="1">¹Adjusted for gender, household wealth, and maternal education.</footnote>

<footnote n="2">²Additionally adjusted for sample storage time, that showed a negative correlation with fasting insulin level (Spearman's ρ = -0.2, P < 0.001). Statistically significant coefficients are presented in bold.</footnote>
<table><caption>TABLE 5 Overall and gender-stratified associations of dietary diversity score (DDS) quartiles with anthropometric and blood markers of cardiometabolic risk among the Maternal and Infant Nutrition Intervention in Matlab (MINIMat) adolescents.</caption><thead><tr><th rowspan="2">Marker</th><th rowspan="2"></th><th colspan="2">All (N = 2,253)</th><th colspan="2">Boys (N = 1,079)</th><th colspan="2">Girls (N = 1,174)</th></tr><tr><th>β (95% CI)</th><th>P</th><th>β (95% CI)</th><th>P</th><th>β (95% CI)</th><th>P</th></tr></thead><tbody><tr><td rowspan="4">Ln WC Adjusted<sup>1</sup><br>model</td><td>Q1 (DDS ≤ 4)</td><td>Ref</td><td></td><td>Ref</td><td></td><td>Ref</td><td></td></tr><tr><td>Q2 (DDS = 5)</td><td>0.003 (−0.008, 0.013)</td><td>0.604</td><td>0.012 (−0.003, 0.027)</td><td>0.133</td><td>−0.005 (−0.020, 0.010)</td><td>0.550</td></tr><tr><td>Q3 (DDS = 6)</td><td>−0.009 (−0.020, 0.003)</td><td>0.153</td><td>−0.012 (−0.028, 0.004)</td><td>0.140</td><td>−0.005 (−0.022, 0.013)</td><td>0.598</td></tr><tr><td>Q4 (DDS ≥ 7)</td><td>0.009 (−0.004, 0.023)</td><td>0.178</td><td><strong>0.019 (0.0001, 0.037)</strong></td><td><strong>0.048</strong></td><td>−0.001 (−0.020, 0.019)</td><td>0.945</td></tr><tr><td rowspan="4">SBP Adjusted<sup>1</sup><br>model</td><td>Q1 (DDS ≤ 4)</td><td>Ref</td><td></td><td>Ref</td><td></td><td>Ref</td><td></td></tr><tr><td>Q2 (DDS = 5)</td><td><strong>0.913 (0.094, 1.732)</strong></td><td><strong>0.029</strong></td><td>1.158 (−0.010, 2.415)</td><td>0.071</td><td>0.706 (−0.367, 1.779)</td><td>0.197</td></tr><tr><td>Q3 (DDS = 6)</td><td>0.140 (−0.764, 1.044)</td><td>0.761</td><td>0.167 (−1.157, 1.492)</td><td>0.804</td><td>0.092 (−1.148, 1.333)</td><td>0.884</td></tr><tr><td>Q4 (DDS ≥ 7)</td><td><strong>1.413 (0.373, 2.453)</strong></td><td><strong>0.008</strong></td><td><strong>2.078 (0.543, 3.612)</strong></td><td><strong>0.008</strong></td><td>0.704 (−0.710, 2.118)</td><td>0.329</td></tr><tr><td rowspan="4">Ln TG Adjusted<sup>1</sup><br>model</td><td>Q1 (DDS ≤ 4)</td><td>Ref</td><td></td><td>Ref</td><td></td><td>Ref</td><td></td></tr><tr><td>Q2 (DDS = 5)</td><td>0.019 (−0.023, 0.060)</td><td>0.384</td><td>0.051 (−0.012, 0.114)</td><td>0.112</td><td>−0.007 (−0.063, 0.048)</td><td>0.795</td></tr><tr><td>Q3 (DDS = 6)</td><td>−0.016 (−0.063, 0.030)</td><td>0.487</td><td>0.003 (−0.063, 0.070)</td><td>0.922</td><td>−0.035 (−0.099, 0.030)</td><td>0.290</td></tr><tr><td>Q4 (DDS ≥ 7)</td><td>−0.003 (−0.056, 0.050)</td><td>0.906</td><td>0.061 (−0.016, 0.138)</td><td>0.120</td><td>−0.070 (−0.143, 0.003)</td><td>0.061</td></tr><tr><td rowspan="4">TC Adjusted<sup>1</sup><br>model</td><td>Q1 (DDS ≤ 4)</td><td>Ref</td><td></td><td>Ref</td><td></td><td>Ref</td><td></td></tr><tr><td>Q2 (DDS = 5)</td><td>0.009 (−0.059, 0.077)</td><td>0.794</td><td>0.053 (−0.038, 0.145)</td><td>0.252</td><td>−0.027 (−0.126, 0.072)</td><td>0.595</td></tr><tr><td>Q3 (DDS = 6)</td><td>0.028 (−0.047, 0.102)</td><td>0.468</td><td>0.018 (−0.079, 0.114)</td><td>0.719</td><td>0.041 (−0.073, 0.155)</td><td>0.477</td></tr><tr><td>Q4 (DDS ≥ 7)</td><td>0.002 (−0.083, 0.088)</td><td>0.954</td><td>0.057 (−0.054, 0.169)</td><td>0.316</td><td>−0.055 (−0.185, 0.075)</td><td>0.407</td></tr><tr><td rowspan="4">LDL Adjusted<sup>1</sup><br>model</td><td>Q1 (DDS ≤ 4)</td><td>Ref</td><td></td><td>Ref</td><td></td><td>Ref</td><td></td></tr><tr><td>Q2 (DDS = 5)</td><td>−0.008 (−0.066, 0.049)</td><td>0.772</td><td>0.022 (−0.054, 0.098)</td><td>0.573</td><td>−0.033 (−0.118, 0.053)</td><td>0.456</td></tr><tr><td>Q3 (DDS = 6)</td><td>0.006 (−0.058, 0.070)</td><td>0.854</td><td>−0.023 (−0.103, 0.056)</td><td>0.565</td><td>0.038 (−0.060, 0.137)</td><td>0.446</td></tr><tr><td>Q4 (DDS ≥ 7)</td><td>0.010 (−0.063, 0.083)</td><td>0.782</td><td>0.045 (−0.047, 0.138)</td><td>0.340</td><td>−0.029 (−0.142, 0.084)</td><td>0.612</td></tr><tr><td rowspan="4">HDL Adjusted<sup>1</sup><br>model</td><td>Q1 (DDS ≤ 4)</td><td>Ref</td><td></td><td>Ref</td><td></td><td>Ref</td><td></td></tr><tr><td>Q2 (DDS = 5)</td><td>−0.001 (−0.024, 0.022)</td><td>0.929</td><td>−0.003 (−0.037, 0.031)</td><td>0.855</td><td>−0.0002 (−0.032, 0.031)</td><td>0.990</td></tr><tr><td>Q3 (DDS = 6)</td><td><strong>0.032 (0.006, 0.057)</strong></td><td><strong>0.014</strong></td><td><strong>0.045 (0.009, 0.080)</strong></td><td><strong>0.014</strong></td><td>0.019 (−0.017, 0.056)</td><td>0.299</td></tr><tr><td>Q4 (DDS ≥ 7)</td><td>−0.020 (−0.049, 0.009)</td><td>0.182</td><td>−0.040 (−0.081, 0.001)</td><td>0.056</td><td>0.004 (−0.037, 0.045)</td><td>0.849</td></tr><tr><td rowspan="4">Ln HOMA-IR<br>Adjusted<sup>1,2</sup><br>model</td><td>Q1 (DDS ≤ 4)</td><td>Ref</td><td></td><td>Ref</td><td></td><td>Ref</td><td></td></tr><tr><td>Q2 (DDS = 5)</td><td>0.052 (−0.026, 0.130)</td><td>0.191</td><td>0.076 (−0.047, 0.198)</td><td>0.226</td><td>0.027 (−0.072, 0.127)</td><td>0.594</td></tr><tr><td>Q3 (DDS = 6)</td><td>−0.034 (−0.121, 0.052)</td><td>0.435</td><td>−0.013 (−0.144, 0.117)</td><td>0.839</td><td>−0.049 (−0.163, 0.066)</td><td>0.406</td></tr><tr><td>Q4 (DDS ≥ 7)</td><td>−0.013 (−0.113, 0.085)</td><td>0.788</td><td>−0.018 (−0.168, 0.132)</td><td>0.812</td><td>−0.003 (−0.134, 0.128)</td><td>0.965</td></tr></tbody></table>

Ref, reference category; Ln, natural-log transformed variable where the base of the log was 2.71828; WC, waist circumference; SBP, systolic blood pressure; TG, triglyceride; TC, total cholesterol; LDL, low-density lipoprotein; HDL, high-density lipoprotein; HOMA-IR, insulin resistance from Homeostasis Model Assessment; DDS, dietary diversity score; Q, quartile.

This study complements a growing body of literature (50) on the links between DPs and cardiometabolic risk among adolescents. Key strengths of the study include: a moderately large sample size based on a well-characterized, rural birth cohort (26), application of a double-pass method for dietary assessment that offered cost-effectiveness, high inter-rater reliability and low respondent burden while minimizing recall bias, and combination of *a priori* and *a posteriori* approaches to analyzing DPs. The findings are generalizable to adolescents in Matlab because of the area-wide recruitment of pregnant women in the MINIMat trial (25) and also to other rural, agrarian settings in Bangladesh due to the similarity in socio-cultural context. However, some critical limitations of this study need to be acknowledged. Drawing any causal inference from the associations

would be erroneous owing to the cross-sectional design. We did not ascertain portion size and could not adjust the analyses for total energy intake. A single, 24-hour recall might not be entirely representative of the adolescents' habitual dietary consumption. Under-reporting of sweet and savory snacks and SSBs in a 24-hour recall has been documented among LMIC adolescents (64, 65), but a recent validation study demonstrates the degree of under-reporting to be acceptable (64)–especially with larger samples. Although the decision to fix the number of clusters at four was based on the scree plot and the size and interpretability of clusters in Matlab context, it may limit comparability with other studies. Finally, we could not completely rule out residual confounding.

<footnote n="1">¹Adjusted for gender, household wealth, and maternal education.</footnote>

<footnote n="2">²Additionally adjusted for sample storage time, that showed a negative correlation with fasting insulin level (Spearman's ρ = −0.2, P < 0.001). Statistically significant coefficients are presented in bold.</footnote>