Dietary Energy and Nutrient Intake Among Pregnant Women in Sri Lanka: Findings From the Sri Lanka Mother and Newborn Growth Cohort Study

ABSTRACT

Background

Maternal nutrition during pregnancy is crucial to ensure positive outcomes for both the mother and newborn. Despite its importance, maternal nutritional status remains poor, particularly in low‐ and middle‐income countries, where social, cultural and economic factors play a pivotal role. This study assessed the adequacy of energy and dietary nutrient intake among Sri Lankan pregnant women compared to their dietary reference intakes.

Methods

This study was part of the Sri Lanka Mother and Newborn Growth study, a nationwide prospective cohort of 2000 pregnant women. We prospectively investigated maternal dietary intake using a validated food frequency questionnaire during the first, second and third trimesters.

Results

We observed an imbalanced macronutrient distribution, with carbohydrates contributing over 67% of total median energy intake, while protein and fat contributed approximately 11% and 17%, respectively. Ethnic and socio‐economic disparities in energy and macronutrient intake were notable. The Sinhalese ethnic group showed the highest median percentage of energy consumption derived from carbohydrates at 68.0% (IQR: 64.6%‒71.1%), significantly exceeding the intake of Tamil and Moor groups. Estate residents reported the lowest proportion of energy from protein (10.6%, IQR: 10.1%‒11.3%), which was significantly lower than the intakes of urban and rural residents. Additionally, women in the lowest income group had a lower total energy intake (1871 kcal/day, IQR: 1464–2392) than those in higher income groups, highlighting socio‐economic influences on maternal nutrition. Micronutrient deficiencies were pervasive, particularly for vitamin B2 (88.6%–91.0%), folate (99.3%–99.8%), vitamin B12 (97.8%–99.5%), calcium (80.8%–91.4%) and iron (91.1%–96.5%). Sodium intake exceeded recommended levels for over 75% of women across all trimesters.

Conclusion

Imbalances in macronutrient intake and widespread micronutrient deficiencies among Sri Lankan pregnant women underscore the urgent need for interventions targeting maternal nutrition. Addressing socio‐economic disparities is critical to improving maternal and neonatal health outcomes.

Summary

• Maternal nutrition is a critical determinant of positive pregnancy outcomes.

• Individuals' social and economic characteristics can influence their diets.

• The unmet energy and dietary nutrient requirements among pregnant women highlight the need for urgent intervention in the Sri Lankan context.

1. Introduction

Maternal nutrition during pregnancy plays a crucial role in ensuring optimal foetal growth and favourable obstetric outcomes [1]. Poor maternal nutrition has been linked to low birth weight (LBW) (< 2500 g at birth), small for gestational age (< 10% birth weight for gestational age), intrauterine growth restriction [1], and an increased risk of chronic diseases later in life [2]. Malnutrition refers to a state in which energy and/or nutrient intake is deficient, excessive or imbalanced [3]. Malnutrition in one or more forms is reported to affect every country globally, with women, children and adolescents in low‐ and middle‐income countries being particularly affected, especially by undernutrition and micronutrient deficiencies [3]. A low‐quality or insufficient diet leads to inadequate nutritional intake, causing these deficiencies. Moreover, women in low‐income countries are more likely to experience malnutrition due to factors, such as food insecurity, frequent infections, inadequate healthcare, heavy labour loads and gender inequality [4]. Women in these regions typically begin pregnancy in a state of malnutrition and struggle to meet pregnancy‐related nutritional requirements because of chronically poor diets [5]. Pregnancy exacerbates these nutrient deficiencies, leading to various adverse outcomes, including miscarriage, stillbirth, birth defects, small for gestational age and preterm birth [6].

Despite substantial evidence supporting the importance of adequate nutrition during pregnancy, nutritional deficiencies remain prevalent, contributing to high rates of morbidity and mortality worldwide. According to the World Health Organization (WHO), the global prevalence of anaemia (haemoglobin level of < 110 g/L) in pregnant women was 36.5% in 2019, with Southeast Asia reporting the highest prevalence (47.8%), followed by Africa (45.8%) [7]. Iron deficiency is the most common nutritional cause of anaemia, although deficiencies in folate, vitamin B12 and vitamin A also play significant roles. From 1995 to 2005, the worldwide prevalence of vitamin A deficiency in pregnant women, based on serum retinol levels of < 0.70 µmol/L, was 15.3%, with Southeast Asia showing the second highest prevalence (17.3%) among the six WHO regions [8]. Iodine deficiency continues to be a global public health issue, affecting both developing and developed countries [9]. Although global estimates for other micronutrient deficiencies are limited, population‐based studies in South Asia have identified deficits in zinc (15%–74%), vitamin B12 (19%–74%), vitamin E (50%–70%) and folate (0%–26%) among pregnant women [10, 11, 12, 13, 14, 15, 16]. While knowledge of a population's nutrient intake is essential for developing effective intervention strategies, the high cost of assessing individual nutrient biomarkers has limited large‐scale population estimates, hindering efforts to fully understand the impact of nutritional deficiencies. However, in the absence of sufficient biomarker data, quantitative dietary assessments offer an effective, low‐cost method for evaluating nutrient intake adequacy against dietary reference intakes (DRIs) at the population level [6].

Sri Lanka, a lower‐middle‐income country in South Asia with a population of 21 million, is home to diverse ethnicities, including Sinhalese, Tamil, Moor and others [17]. Despite relatively good national health indicators during the past two decades, LBW remains a significant public health concern. National health statistics indicate that approximately 16% of infants in Sri Lanka are born with LBW, while one in three pregnant women develops anaemia [18]. In 2020, national health data showed a decrease in the prevalence of underweight (body mass index [BMI] of < 18.5 kg/m²) among women in their first trimester, from 24.6% in 2011 to 14.7%. However, the prevalence of overweight and obesity (BMI of ≥ 25 kg/m²) increased from 15.2% to 31.3% over the same period [18]. In 2021, Sri Lanka faced its most severe economic downturn in recent history, resulting in widespread shortages of food, fuel, gas and other essential goods. Food prices consequently surged, including those for staples, such as rice, vegetables and fruits, reaching record levels of inflation by mid‐2022. This economic crisis has made it increasingly difficult for Sri Lankans to meet basic nutritional needs. According to the World Food Programme, more than 28% of the population experienced moderate acute food insecurity and required humanitarian assistance during this period [19]. To date, no national‐level studies have evaluated the adequacy of energy and dietary nutrient intake among pregnant women in Sri Lanka. Therefore, the current study was performed to quantitatively assess the adequacy of dietary energy and macro‐ and micronutrient intake in a nationally representative sample of Sri Lankan pregnant women. The findings are expected to provide evidence‐based dietary nutrient data to inform the development of effective nutritional interventions for pregnant women in Sri Lanka and other countries with similar socioeconomic conditions.

2. Methods

2.1. The Sri Lanka Mother and Newborn Growth (S‐MaNGro) Cohort Study

We conducted a prospective nutritional survey among pregnant women as part of the S‐MaNGro cohort study. The S‐MaNGro study was a nationwide, prospective cohort study carried out between August 2022 and April 2024. A more detailed description of the S‐MaNGro methodology has been published elsewhere [20]. In brief, pregnant women of Sri Lankan nationality who were receiving antenatal care at government‐sponsored clinics during the first trimester of pregnancy (< 12 weeks of gestation) and who did not have major chronic diseases or a history of psychiatric disorders were included in the study. The estimated sample size was 1722 pregnant women, providing a statistical power of 80% and a significance level of 5%. To account for an expected 15%–20% loss to follow‐up, 2000 first‐trimester pregnant women were recruited, representing all 25 districts of Sri Lanka. The public antenatal care in Sri Lanka is primarily provided through community healthcare centres that are part of a well‐designed network of health units called Medical Officer of Health (MOH) areas. Each district is divided into these MOH areas, where our primary sampling units, the antenatal clinics, are located. The sampling process commenced by including all 25 districts, followed by randomly selecting one to four MOH areas within each district proportionate to the district's population density. The MOH areas were selected using a simple random sampling technique, whereby numbers were drawn to determine the specific areas for inclusion. The number of participants from each district was calculated using the probability proportional to size method, which accounts for the population distribution within each district. Once the sample sizes were determined, participants were recruited from the antenatal clinics within the selected MOH areas using convenience sampling, continuing until the required sample size for each district was achieved. The women recruited during the first trimester of pregnancy were followed up until delivery. The first‐trimester interview, conducted at the time of recruitment (< 12 weeks gestation), was considered the baseline survey. Follow‐up interviews were conducted once during the second trimester (20–26 weeks gestation) and once during the third trimester (28–36 weeks gestation) pregnancy.

2.2. Sociodemographic Data

We collected sociodemographic data using an interviewer‐administered structured questionnaire during the baseline survey. The questionnaire included information on maternal age, marital status, ethnicity, education level, area of residence, employment status and monthly household income. The baseline survey was conducted through face‐to‐face interviews facilitated by trained field assistants.

2.3. Anthropometric Data

We extracted data on the women's body weight and BMI from their first antenatal clinic visit (usually at approximately 6–8 weeks of gestation), as recorded on individual pregnancy cards. The body weight recorded at this first visit was considered the pre‐pregnancy weight, and the BMI was regarded as the pre‐pregnancy BMI. Maternal height was calculated using the weight and BMI data from this first antenatal clinic visit. The classification of BMI was based on the international BMI cut‐off values defined by the WHO [21].

2.4. Data on Dietary Behaviour and Food Environment

Data on the women's food choices, dietary behaviour and food environment were collected during the baseline survey using a structured questionnaire administered by trained field assistants. This questionnaire was developed explicitly for this study, drawing insights from previous literature on dietary behaviour and food environments and discussions with research team members specialized in midwifery and nutrition epidemiology. It included questions on dietary preferences, eating habits, meal frequency, meal skipping behaviours, food preparation practices and questions asking about the local food environment, such as the accessibility to a variety of fruits, vegetables, fish and meat, as well as family support in meal preparation during pregnancy. Before data collection, the questionnaire was pre‐tested on a small sample of pregnant women to ensure the clarity and relevance of the questions.

2.5. Dietary Intake Data

We used a food frequency questionnaire (FFQ) validated for the Sri Lankan population [22] to assess the nutritional intake of pregnant women during each trimester. The FFQ includes more than 85 food items and 12 food photographs to help participants understand portion sizes. Alongside the administration of the FFQ, we gathered data on maternal consumption of Thriposha, a fortified blended food product, including information on portion sizes and frequency of intake. However, data on maternal iron and folate supplement intake were not collected. Thriposha supplements a regular diet with essential nutrients and is distributed free of charge to pregnant and lactating mothers, as well as undernourished children, through the national nutrition programme to improve their nutritional status. Thriposha is a precooked, ready‐to‐eat food made from maize, soya, whole cream milk powder, vitamins and minerals. Each 100 g of the product contains 401.8 kcal of energy, 61.9 g of carbohydrates, 20.0 g of protein, 7.8 g of fat, 1700 IU of vitamin A and 18 mg of iron [23]. Beneficiaries are entitled to receive two packets of Thriposha (750 g each) per month and are recommended to consume 50 g daily.

The first‐trimester dietary assessment was conducted during the baseline survey. A colour copy of the food photographs included in the FFQ was provided to each pregnant woman during the baseline survey for use in the subsequent nutritional assessments during the second and third trimesters. The second‐ and third‐trimester nutritional assessments were conducted by trained interviewers in the participants' native language, over the phone at 20–26 weeks and 28–36 weeks of gestation, respectively.

2.6. Nutrient Intake by Pregnant Women

The nutritional content of the collected dietary data was analyzed using the NutriSurvey 2007 nutrient analysis software (EBISpro, Willstätt, Germany), which was modified for Sri Lankan food. The modification involved incorporating the Sri Lankan food composition tables into the software [24]. Missing information from the Sri Lankan food composition tables was supplemented using data from the United States Department of Agriculture database [25]. Nutritional information on food labels was used for processed foods, snacks and biscuits. More detailed information on the software modification has been published elsewhere [20].

2.7. Estimated Energy Requirement (EER) During Pregnancy

Nutritional needs increase during pregnancy to meet the additional metabolic and tissue demands of both the mother and the developing foetus. Energy requirements in pregnant women remain similar to those of non‐pregnant women during the first trimester. However, in the second and third trimesters, these requirements increase by 340 and 452 kcal per day, respectively. Total energy requirements vary based on factors, such as age, BMI and physical activity level [26]. Because energy requirements differ by age, we calculated the EER for non‐pregnant women separately for those < 19 years of age and for adult mothers aged ≥ 19 years [26] as follows:

$$\text{EER}(\text{non}‐\text{pregnant})<19\mathrm{years}=135.3‐(30.8\times \text{age}[\mathrm{year}\left]\right)+\text{physical}\text{activity}\text{coefficient}\times \left[\right(10.0\times \text{weight}\left[\text{kg}\right])+(934\times \text{height}\left[\mathrm{m}\right]\left)\right]+25,$$

$$\text{EER}(\mathrm{non}‐\mathrm{pregnant})\ge 19\mathrm{years}=354‐(6.91\times \text{age}[\mathrm{years}\left]\right)+\text{physical}\text{activity}\text{coefficient}\times \left[\right(9.36\times \text{weight}\left[\text{kg}\right])+(726\times \text{height}\left[\mathrm{m}\right]\left)\right].$$

The values for the active physical level, 1.31 for women aged < 19 years and 1.27 for women aged ≥ 19 years, were used as the physical activity coefficient [26], assuming all pregnant women remained active throughout the pregnancy period.

Subsequently, the EER for each trimester was calculated as follows [26]:

$$\text{EER}(\text{first}\text{trimester})=\text{EER}(\text{non}‐\text{pregnant}),$$

$$\text{EER}(\text{second}\text{trimester})=\text{EER}(\text{non}‐\text{pregnant})+340\text{kcal},$$

$$\text{EER}(\text{third}\text{trimester})=\text{EER}(\text{non}‐\text{pregnant})+452\text{kcal}.$$

2.8. Statistical Analysis

We analyzed the data using Minitab statistical software version 22.1. A p‐value of < 0.05 was considered statistically significant. For continuous data, participants' characteristics are presented as mean and SD, while categorical data are presented as frequency and percentage. Dietary behaviour and food environment data are also shown as percentages and frequencies. The energy and nutrient analysis of the collected FFQ data was performed using the modified NutriSurvey 2007 software, which was then transferred to Microsoft Excel and subsequently to Minitab for statistical analysis. We identified and excluded extremely unrealistic energy intake data by removing participants with intakes ± 2 SDs from the mean energy intake in each trimester.

Macronutrient and micronutrient data were energy‐adjusted to minimize measurement error, which is common with dietary assessment questionnaires [27, 28]. During the energy adjustment, macronutrients (carbohydrates, protein, fat and fatty acids) were expressed as a proportion of energy, while micronutrients were expressed as intake (in appropriate units) per 1000 kcal. Nutrient data are presented as mean ± SD and as median with corresponding interquartile range (IQR). Energy intake data are displayed as whole numbers, while macronutrient and fibre data are shown to one decimal place and micronutrient data to two decimal places. Because the nutrient data deviated from normal distributions, comparisons of women's nutrient intake across the three trimesters were performed using the Friedman test followed by post hoc comparisons with Bonferroni correction. Energy‐adjusted macronutrient intakes were compared with the respective acceptable micronutrient distribution ranges (AMDRs). The influence of women's sociodemographic factors on first‐trimester energy and energy‐adjusted macronutrient intakes was assessed using the Kruskal–Wallis test followed by post hoc Bonferroni correction when there were more than two categories, or using the Mann–Whitney U‐test for comparisons between two categories. The adequacy of dietary nutrient intake was assessed by comparing the nutrient intake estimated by the FFQ to the National Academies of Sciences, Engineering and Medicine's DRI recommendations [29]. Appendix 1 provides DRIs for selected nutrients during pregnancy. Because DRIs for micronutrients are expressed as amounts per day, each reference value was energy‐adjusted as explained in a previous study [30], as follows:

$$\text{Energy}-\text{adjusted DRI}=\frac{\text{DRI}\left(\text{amount perday}\right)}{\text{EER}\left(\text{kcal per day}\right)}\times 1000\mathrm{kcal}.$$

For nutrients with AMDR, namely carbohydrates, protein and total fat, energy‐adjusted intake levels below the lower limit of the AMDR were considered inadequate. For nutrients with estimated average requirement (EAR), which include vitamin A, vitamin B1, vitamin B2, niacin, vitamin B6, folate, vitamin B12, vitamin C, calcium, magnesium, phosphorus, iron, zinc and copper, observed energy‐adjusted intake levels below the energy‐adjusted EAR were considered inadequate. Furthermore, for nutrients with a tolerable upper intake level (UL), including vitamin A, niacin, vitamin B6, folate, vitamin C, calcium, magnesium, phosphorus, iron, zinc, copper, sodium and manganese, the energy‐adjusted UL was used as the threshold for determining the proportion of participants exceeding the UL. Because it is difficult to assess inadequacy for nutrients with adequate intake (AI), even if intake levels fall below AI [31], no comparison was made based on AI for dietary fibre, sodium, potassium and manganese.

3. Results

3.1. Participants' Characteristics

A misdiagnosis of pregnancy led to the exclusion of one of the 2000 pregnant women recruited for the cohort study from the data analysis. Of the 1999 remaining participants, most were between 25 and 34 years old (64.1%), were Sinhalese (74.8%), lived in rural areas (57.8%) and had a monthly household income between 35,000 and 59,999 Sri Lankan rupees (LKR) (41.8%). The distribution of ethnicities in the study sample closely mirrors national‐level statistics [32]. The mean pre‐pregnancy BMI in the total sample was 23.2 ± 4.6 kg/m², with a prevalence of underweight at 15.2%. Among multiparous women, 14.1% had a history of giving birth to an LBW baby. The prevalence of anaemia (haemoglobin level of < 110 g/L) was 16.0% in the first trimester and 31.5% in the third trimester (Table 1).

Table 1.

Sociodemographic characteristics of the S‐MaNGro study participants (n = 1999).

Characteristics		Valuea
Maternal age (years)b (n = 1993)		28.8 ± 5.3
Maternal age group (n = 1993) (years)
	≤ 19	68 (3.4%)
	20–24	373 (18.7%)
	25–29	677 (34.0%)
	30–34	599 (30.1%)
	≥ 35	276 (13.8%)
Marital status (n = 1999)
	Married	1995 (99.8%)
	Single	4 (0.2%)
Ethnicity (n = 1999)
	Sinhalese	1496 (74.8%)
	Tamil	321 (16.1%)
	Moor	180 (9.0%)
	Others	2 (0.1%)
Level of Maternal educationc (n = 1995)
	No school education	3 (0.2%)
	Below or up to primary education	49 (2.5%)
	Below or up to O/L	911 (45.7%)
	Below or up to A/L	745 (37.3%)
	Higher	287 (14.4%)
Area of residence (n = 1999)
	Urban	252 (12.6%)
	Sub‐urban	523 (26.2%)
	Rural	1156 (57.8%)
	Estate	68 (3.4%)
Employability (n = 1989)
	Homemaker	1479 (74.4%)
	Employed	510 (25.6%)
Monthly household income (LKR) (n = 1967)
	< 35,000	491 (25.0%)
	35,000–59,999	823 (41.8%)
	≥ 60,000	653 (33.2%)
Pre‐pregnancy BMI (kg/m2)b (n = 1913)		23.2 ± 4.6
Category of pre‐pregnancy BMI (n = 1913)
	Underweight (< 18.5 kg/m2)	291 (15.2%)
	Normal (18.5‒24.9 kg/m2)	999 (52.2%)
	Overweight (25.0‒29.9 kg/m2)	462 (24.2%))
	Obese (≥ 30 kg/m2)	161 (8.4%)
Parityd (n = 1999)
	Nulliparous/Primiparous	933 (46.7%)
	Multiparous	1066 (53.3%)
Having a history of pregnancy loss (n = 1999)		378 (18.9%)
Having a history of delivering a LBW babye (n = 1066)		150 (14.1%)
Maternal anaemia during the first trimester of pregnancy (n = 1745)		279 (16.0%)
Maternal anaemia during the third trimester of pregnancy (n = 1494)		470 (31.5%)

Abbreviations: A/L, advanced level; BMI, body mass index; LBW, low birth weight; LKR, Sri Lankan rupee; O/L, ordinary level.

^a Data are presented as frequency and percentage unless otherwise indicated.

^b Data are presented as means ± SDs.

^c Sri Lanka's public education system offers a 13‐year schooling education, which includes primary school (grades 1–5; ages 6–10), O/L (grades 10–11; ages 16–17) and A/L (grades 12–13; ages 18–19).

^d Women who had at least one live birth were considered multiparous.

^e Considered only the multiparous women.

3.2. Dietary Behaviour and Food Environment During First Trimester of Pregnancy

The reported mean share of monthly income spent on food was 51.7% ± 0.2%. Of all participants, 28.3% reported certain dietary restrictions. Among these, the most common reason for avoidance of certain food items was personal preference (including religious reasons), followed by food allergies, vegetarianism and veganism. The majority of women (76.1%) reported practising good nutritional habits, such as eating a green leaf salad called Mallung at least once daily. Mallung is a cost‐effective dish made from shredded leafy greens, freshly grated coconut, lime, spices and chilli. It can be easily prepared using various locally available greens and is considered rich in a wide range of vitamins and minerals [33]. Additionally, a smaller proportion of women (18.9%) reported cooking meat products longer than they cooked vegetables to avoid potential microbiological contamination. Surprisingly, nearly one‐quarter of the sample (24.6%) reported skipping at least one meal per day, with breakfast being the most commonly skipped (43.1%). Approximately half of the women (55.1%) reported having a fruit or vegetable garden at home, and a significant proportion (89.1%) indicated they would eat more fruits and vegetables if they were less expensive. Notably, almost all women (97.7%) stated they had sufficient family support to maintain a healthy diet during pregnancy (Table 2).

Table 2.

Dietary behaviour and food environment of pregnant women in the first trimester of pregnancy.

Item				Valuea
Food choice
	Average share of monthly income on food (n = 1838); mean ± SD			51.7 ± 0.2%
	Having a special dietary restrictions (n = 1999)
		No special dietary restriction		1433 (71.7%)
		Do not eat some selected food due to personal preferences		353 (17.7%)
		Do not eat some selected food due to food allergies		116 (5.8%)
		Vegetarian		61 (3.0%)
		Vegan		36 (1.8%)
	Having special dietary practices (n = 1506)
		Cooking fish and meat longer than usual (overcooking)		284 (18.9%)
		Prefer to have a green leaves salad at least once a day		1147 (76.1%)
		Other		75 (5.0%)
Eating behaviour
	Number of main meals/day (n = 1993); mean ± SD			2.9 ± 0.4
	Number of snack times/day (n = 1854); mean ± SD			2.1 ± 1.0
	Time spent consuming a main meal in minutes (n = 1966); mean ± SD			16.3 ± 7.1
	Usual speed of taking meals (n = 1991)
		Fast eater		371 (18.7%)
		Slow eater		835 (41.9%)
		Taking an average time		785 (39.4%)
	Skipping meals (n = 1990)
		No		1500 (75.4%)
		Yes		490 (24.6%)
			Skip the breakfast mostly	211 (43.1%)
			Skip the lunch mostly	128 (26.2%)
			Skip the dinner mostly	150 (30.7%)
Food environment
	Distance from home to fruit/vegetable market in km (n = 1842); median (IQR)			2 (3)
	Variety of fruits and vegetables available in the market (n = 1988)
		Good		1178 (59.3%)
		Average		806 (40.5%)
		Bad		4 (0.2%)
	Quality of fruits and vegetables available in the market (n = 1985)
		Good		1118 (56.3%)
		Average		862 (43.4%)
		Bad		5 (0.3%)
	Having a fruit/vegetable garden at home (n = 1988)			1096 (55.1%)
	Prefer to eat more fruits/vegetables if they are less expensive (n = 1990)			1773 (89.1%)
	Mode of transportation from home to market (n = 1825)
		On foot		385 (21.1%)
		Private transport		1086 (59.5%)
		Public transport		354 (19.4%)
	Prefer to cook during the pregnancy (n = 1991)			1079 (54.2%)
	Receiving support for preparing meals from (n = 1890)
		Husband		658 (34.8) %
		Mother		656 (34.7%)
		Mother‐in‐law		483 (25.6%)
		Other		93 (4.9%)
	Having enough family support for a healthy diet during pregnancy (n = 1993)			1947 (97.7%)

Abbreviations: IQR, interquartile range; SD, standard deviation.

^a Data are presented as frequency and percentage unless otherwise indicated.

3.3. Energy and Macro‐ and Micronutrient Intake by Pregnant Women

Of the 1999 women, 1870, 1805 and 1620 responded to the FFQ in the first, second and third trimesters, respectively. After excluding unrealistic energy intake data, the final dietary analysis included 1791, 1756 and 1577 data points for the first, second and third trimesters, respectively. A significant difference in maternal energy intake was observed across the three trimesters of pregnancy. Figure 1 shows the distribution of energy‐adjusted maternal macronutrient intake compared to the AMDR for each macronutrient [29]. The percentage of energy derived from protein for most participants (80.3%–87.6%) was within the AMDR, while 61.2%–67.0% of women reported energy derived from fat below the AMDR in all three trimesters. For more than two‐thirds of participants (66.6%–72.2%), the energy‐adjusted carbohydrate intake was above the AMDR in all three trimesters.

Figure 1.

Distribution of maternal macronutrient intake based on acceptable macronutrient distribution ranges (AMDR) defined National Academies of Sciences, Engineering and Medicine's DRI recommendations [29]. AMDR for adolescents < 19 years: fat 25%‒35% of energy, protein 10%‒30% of energy and carbohydrate 45%‒65% of energy. AMDR for adults ≥ 19 years: fat 20%‒35% of energy, protein 10%‒35% of energy and carbohydrate 45%‒65% of energy.

Table 3 presents the means ± SDs and medians (IQRs) for pregnant women's energy and energy‐adjusted macro‐ and micronutrient intake across all three trimesters. This part of the analysis was limited to women with dietary data for all three trimesters (n = 1395). The highest estimated median energy intake was observed in the second trimester, at 2343 (1811‒2734) kcal/day, followed by the third trimester at 2225 (1651‒2653) kcal/day and the first trimester at 1972 (1560‒2435) kcal/day. In the second trimester, women had significantly lower energy‐adjusted median protein intake than in the first trimester, while the first trimester had the lowest median carbohydrate intake. Conversely, the median intakes of folate, vitamin B12 and iron were higher in the first trimester than in the second and third trimesters. All three trimesters showed a median energy‐adjusted saturated fatty acid intake of ≥ 10%, with no significant differences between trimesters. Similarly, sodium intake did not differ significantly across trimesters.

Table 3.

Energy and energy‐adjusted maternal dietary intake during pregnancy.

	Energy and macro‐ and micronutrient intake						p value
	First trimester of pregnancy (n = 1395)		Second trimester of pregnancy (n = 1395)		Third trimester of pregnancy (n = 1395)
	Mean ± SD	Median (IQR)	Mean ± SD	Median (IQR)	Mean ± SD	Median (IQR)
Energy (kcal/day)	2016 ± 592	1972 (1560‒2435)a	2272 ± 600	2343 (1811‒2734)b	2183 ± 618	2225 (1651‒2653)c	< 0.001**
Protein (E%)	11.1 ± 1.4	11.0 (10.2‒11.9)a	11.0 ± 1.0	10.9 (10.4‒11.5)b	11.1 ± 1.1	11.0 (10.4‒11.7)a,b	< 0.001**
Fat (E%)	19.2 ± 4.9	18.8 (15.7‒22.0)a	18.5 ± 4.8	17.7 (15.1‒21.3)b	18.4 ± 4.6	17.8 (15.1‒21.3)b	< 0.001**
Carbohydrate (E%)	66.8 ± 5.6	67.5 (63.5‒70.7)a	67.8 ± 5.4	68.8 (64.7‒71.5)b	67.8 ± 5.2	68.6 (64.3‒71.5)b	< 0.001**
Saturated fatty acid (E%)	10.9 ± 3.7	10.3 (8.2‒12.8)	11.0 ± 3.7	10.4 (8.3‒13.0)	10.7 ± 3.7	10.0 (8.0‒13.2)	0.04**
Monounsaturated fatty acid (E%)	2.7 ± 0.9	2.6 (2.1‒3.1)a	2.5 ± 0.9	2.3 (1.8‒3.0)b	2.5 ± 0.8	2.4 (1.9‒3.0)b	< 0.001**
Polyunsaturated fatty acid (E%)	1.8 ± 0.5	1.7 (1.4‒2.1)a	1.6 ± 0.5	1.5 (1.3‒2.0)b	1.7 ± 0.5	1.5 (1.3‒2.0)b	< 0.001**
Dietary fibre (g/1000 kcal)	17.3 ± 4.8	16.6 (13.9‒19.6)a	15.9 ± 3.6	15.3 (13.0‒18.3)b	16.2 ± 3.9	15.8 (13.3‒18.9)b	< 0.001**
Vitamin A (µg/1000 kcal)	267.84 ± 111.23	250.60 (193.80‒317.60)a	232.68 ± 83.23	222.48 (177.73‒276.57)b	235.46 ± 86.21	225.59 (177.32‒278.89)b	< 0.001**
Vitamin B1 (mg/1000 kcal)	0.42 ± 0.11	0.41 (0.35‒0.48)a	0.44 ± 0.10	0.43 (0.37‒0.48)b	0.45 ± 0.12	0.44 (0.37‒0.51)b	< 0.001**
Vitamin B2 (mg/1000 kcal)	0.37 ± 0.15	0.33 (0.27‒0.43)a	0.33 ± 0.10	0.30 (0.26‒0.37)b	0.33 ± 0.11	0.30 (0.26‒0.36)b	< 0.001**
Niacin (mg/1000 kcal)	10.38 ± 2.50	10.23 (8.73‒12.00)a	10.89 ± 2.54	10.95 (9.02‒12.85)b	10.67 ± 2.81	10.66 (8.85‒12.87)b	< 0.001**
Vitamin B6 (mg/1000 kcal)	1.18 ± 0.26	1.15 (1.01‒1.31)a	1.14 ± 0.21	1.10 (1.00‒1.23)b	1.16 ± 0.26	1.15 (1.01‒1.31)a	< 0.001**
Folate (µg/1000 kcal)	106.81 ± 31.97	102.43 (84.63‒123.16)a	96.94 ± 22.34	93.80 (81.68‒109.08)b	100.08 ± 25.20	95.70 (83.00‒112.80)c	< 0.001**
Vitamin B12 (µg/1000 kcal)	0.29 ± 0.25	0.24 (0.13‒0.39)a	0.22 ± 0.16	0.20 (0.11‒0.30)b	0.24 ± 0.18	0.21 (0.12‒0.32)c	< 0.001**
Vitamin C (mg/1000 kcal)	55.75 ± 63.58	41.18 (22.88‒67.21)a	46.57 ± 35.70	40.04 (21.72‒60.65)b	47.82 ± 31.94	41.66 (23.45‒62.68)a,b	0.017**
Sodium (mg/1000 kcal)	1431.28 ± 517.52	1363.66 (1091.71‒1720.01)	1378.53 ± 421.87	1315.06 (1103.85‒1569.95)	1416.39 ± 453.29	1346.75 (1109.24‒1637.90)	0.074
Potassium (mg/1000 kcal)	1103.07 ± 284.06	1040.00 (901.31‒1256.45)a	989.40 ± 210.45	942.58 (834.25‒1102.34)b	1009.31 ± 232.93	979.54 (850.02‒1133.56)b	< 0.001**
Calcium (mg/1000 kcal)	260.69 ± 70.46	250.28 (212.99‒297.17)a	247.72 ± 54.65	238.86 (211.41‒275.25)b	250.85 ± 60.52	241.69 (211.24‒282.24)b	< 0.001**
Magnesium (mg/1000 kcal)	172.91 ± 70.53	143.80 (118.23‒223.51)a	163.82 ± 72.04	131.30 (111.10‒199.32)b	173.06 ± 78.47	136.96 (114.41‒223.96)a	< 0.001**
Phosphorus (mg/1000 kcal)	460.80 ± 82.34	445.96 (403.19‒512.96)a	455.80 ± 76.72	437.95 (402.70‒493.26)a	469.16 ± 87.47	454.00 (410.23‒525.46)b	< 0.001**
Iron (mg/1000 kcal)	6.32 ± 1.73	6.08 (5.06‒7.23)a	5.79 ± 1.33	5.60 (4.85‒6.57)b	6.02 ± 1.46	5.83 (5.03‒6.75)c	< 0.001**
Zinc (mg/1000 kcal)	3.76 ± 0.71	3.62 (3.24‒4.22)a	3.68 ± 0.67	3.48 (3.21‒4.06)b	3.76 ± 0.82	3.60 (3.27‒4.34)a	< 0.001**
Copper (mg/1000 kcal)	0.99 ± 0.19	0.96 (0.86‒1.10)a	0.92 ± 0.14	0.90 (0.82‒1.01)b	0.93 ± 0.17	0.92 (0.83‒1.02)b	< 0.001**
Manganese (mg/1000 kcal)	4.63 ± 1.68	4.03 (3.37‒5.56)	4.45 ± 1.64	3.81 (3.35‒4.93)	4.61 ± 1.79	3.97 (3.36‒5.63)	0.05

Note: Maternal dietary intake in three trimesters was compared using the Friedman test followed by the Bonferroni correction post hoc test, and the significant level is 0.05. ^a,b,cValues with the same superscript lowercase letter do not indicate a significant difference, and interpretation should focus on one row at a time.

Abbreviations: E%, as a percentage of total energy intake; IQR, interquartile range; SD, standard deviation.

^** p < 0.05.

Table 4 shows the median (IQR) distribution of first‐trimester energy and energy‐adjusted macronutrient intake among pregnant women based on their sociodemographic profile. The results indicated no significant disparity in energy or energy‐adjusted macronutrient intake based on age. By contrast, maternal education level had a significant impact on dietary energy and energy‐adjusted macronutrient intake. The Sinhalese ethnicity exhibited the highest median energy consumption, with a daily intake of 2018 (1598‒2476) kcal/day, which was significantly higher than the Moor ethnicity's intake of 1837 (1481‒2338) kcal/day. Additionally, the Sinhalese had the highest median percentage of energy derived from carbohydrates, at 68.0% (64.6%‒71.1%), which was significantly different from the percentages for the Tamil and Moor ethnic groups. The Moor ethnic group had the highest energy‐adjusted protein consumption, while the Tamil ethnic group had the highest energy‐adjusted fat consumption. Regarding the area of residence, urban women had the lowest energy intake from carbohydrates (65.3%, IQR: 60.7%‒70.0%), while their energy‐adjusted fat intake (20.5%, IQR: 16.0%‒23.7%) was significantly higher than that of the residents of other areas. Estate residents reported the lowest proportion of energy from protein (10.6%, IQR: 10.1%‒11.3%), which was significantly lower than the intakes of urban and rural residents. Homemakers had significantly lower energy intake (1962 kcal/day, IQR: 1550%–2432%) and energy‐adjusted protein intake (11.0%, IQR: 10.2%–11.9%) than employed women. The lowest income group had lower total energy intake (1871 kcal/day, IQR: 1464–2392) than higher income groups. No significant differences were found in median energy or macronutrient intake based on the maternal pre‐pregnancy BMI category.

Table 4.

First‐trimester energy and energy‐adjusted dietary macronutrient intake by sociodemographic characteristics.

Characteristics		Energy		Carbohydrate		Protein		Fat
		Median (IQR) Kcal/d	p‐value	Median (IQR); E%	p‐value	Median (IQR); E%	p‐value	Median (IQR); E%	p‐value
Maternal age (years)
	≤ 19	1929 (1508‒2433)	0.891	66.4 (59.9‒71.0)	0.520	11.4 (10.0‒12.7)	0.668	19.9 (15.8‒24.8)	0.391
	20–24	1973 (1554‒2446)		67.5 (63.4‒70.9)		11.0 (10.2‒12.0)		18.8 (15.5‒22.0)
	25–35	1995 (1572‒2463)		67.4 (63.5‒70.8)		11.1 (10.2‒11.9)		18.7 (15.7‒22.0)
	> 35	1963 (1601‒2499)		67.6 (64.1‒70.6)		11.0 (10.2‒11.9)		18.3 (15.8‒21.3)
Ethnicity
	Sinhalese	2018 (1598‒2476)a	0.017**	68.0 (64.6‒71.1)a	< 0.001**	11.1 (10.2‒11.9)a	0.019**	18.1 (15.5‒20.9)a	< 0.001**
	Tamil	1945 (1501‒2447)a,b		63.5 (59.0‒68.4)b		10.9 (10.0‒12.0)a		22.5 (17.8‒26.4)b
	Moor	1837 (1481‒2338)b		66.6 (61.7‒70.4)c		11.4 (10.3‒12.6)b		19.4 (15.3‒23.2)c
	Others	2480 (2138‒2821)a,b		64.5 (55.4‒73.6)a,b,c		11.8 (10.4‒13.3)a,b		20.6 (13.0‒28.2)a,b,c
Level of maternal education
	No school education	2336 (1439‒2571)b,c,d	0.010**	66.6 (64.5‒71.0)b,c	< 0.001**	10.3 (9.5‒13.3)b,c	< 0.001**	17.6 (16.3‒21.1)b,c	< 0.001**
	Below or up to primary education	2392 (1798‒2899)b		63.3 (59.9‒68.3)b		11.8 (10.4‒12.6)c,d		22.4 (16.9‒24.8)b
	Below or up to O/L	1951 (1532‒2426)c		67.9 (63.6‒71.3)c		10.9 (10.1‒11.8)b		18.4 (15.4‒22.1)c
	Below or up to A/L	1989 (1595‒2449)a,c,d		67.5 (63.9‒70.6)a,c		11.2 (10.3‒12.0)a,c,d		18.6 (15.7‒21.4)a,c
	Higher	2100 (1597‒2525)b,d		66.6 (62.6‒69.6)b		11.3 (10.3‒12.1)c		19.7 (16.9‒22.6)b
Area of residence
	Urban	2007 (1606‒2447)	0.485	65.3 (60.7‒70.0)a	< 0.001**	11.4 (10.2‒12.3)a	0.016**	20.5 (16.0‒23.7)a	0.001**
	Sub‐urban	2011 (1597‒2437)		67.3 (63.7‒70.4)b		11.0 (10.2‒11.9)a,b		19.0 (16.0‒21.7)b
	Rural	1984 (1560‒2490)		67.6 (63.9‒71.0)b,c		11.1 (10.2‒11.9)a		18.4 (15.5‒21.8)b
	Estate	1915 (1449–2349)		69.3 (65.0‒71.7)c		10.6 (10.1‒11.3)b		17.7 (15.8‒20.9)b
Employabilitya
	Homemaker	1962 (1550‒2432)a	0.003**	67.5 (63.2‒71.1)	0.336	11.0 (10.2‒11.9)a	0.041**	18.7 (15.5‒22.3)	0.665
	Employed	2080 (1663‒2524)b		67.4 (64.0‒70.0)		11.2 (10.3‒12.0)b		18.8 (16.2‒21.2)
Monthly household income
	< 35,000 LKR	1871 (1464‒2392)a	0.001**	67.2 (63.0‒71.0)	0.822	10.9 (10.1‒11.9)	0.079	18.9 (15.5‒22.5)	0.765
	35,000–59,999 LKR	1995 (1588‒2455)b		67.5 (63.4‒71.1)		11.0 (10.2‒11.9)		18.4 (15.5‒22.1)
	≥ 60,000 LKR	2055 (1624‒2514)b		67.5 (64.0‒70.4)		11.2 (10.3‒12.1)		18.7 (15.9‒21.4)
Pre‐pregnancy BMI
	Underweight (< 18.5 kg/m2)	2035 (1534‒2487)	0.204	67.5 (63.3‒70.8)	0.142	11.0 (10.2‒12.0)	0.863	18.4 (15.5‒21.5)	0.140
	Normal (18.5‒24.9 kg/m2)	2016 (1588‒2493)		67.9 (63.8‒71.1)		11.1 (10.2‒11.9)		18.3 (15.5‒21.8)
	Overweight (25.0‒29.9 kg/m2)	1961 (1549‒2409)		67.0 (63.8‒70.5)		11.0 (10.3‒11.8)		18.9 (16.0‒21.8)
	Obese (≥ 30 kg/m2)	1825 (1532‒2432)		66.6 (61.9‒70.6)		10.9 (10.2‒12.1)		19.8 (15.9‒23.3)

Note: ^a,b,c,dValues with the same superscript lowercase letter do not indicate a significant difference, and interpretation should focus on one variable in one column at a time. Compared using the Kruskal–Wallis test followed by Bonferroni correction post hoc test unless otherwise indicated.

Abbreviations: A/L, advanced level; BMI, body mass index; IQR, interquartile range; LKR, Sri Lankan rupee; O/L, ordinary level.

^a Compared using the Mann–Whitney U‐test.

^** p < 0.05.

3.4. Adequacy of Dietary Macro‐ and Micronutrient Intake Among Pregnant Women

Table 5 presents the median energy‐adjusted nutrient intakes and the prevalence of participants who did not meet the DRI recommendations across the three trimesters of pregnancy. The comparison of observed energy‐adjusted nutrient intake against the AMDR revealed that 20% of pregnant women were below the AMDR for protein during the first trimester. The prevalence of inadequate vitamin intake was highest for vitamin B2 (88.6%, 91.0% and 88.5%), folate (99.3%, 99.8% and 99.6%) and vitamin B12 (97.8%, 99.5% and 98.8%) across the trimesters. Among minerals, calcium (91.4%, 86.9% and 80.8%) and iron (95.4%, 96.5% and 91.1%) showed the most significant dietary inadequacies during pregnancy. Additionally, the majority of women in each trimester had dietary sodium consumption above the chronic disease risk reduction intake level (76.9%, 91.6% and 93.5%).

Table 5.

Comparison of energy‐adjusted maternal macro‐ and micronutrient intake with the energy‐adjusted dietary reference intakes.

Nutrient		First trimester of pregnancy (n = 1791)			Second trimester of pregnancy (n = 1756)			Third trimester of pregnancy (n = 1577)
		Median (IQR)	Prevalence of inadequacy (%)	% of individuals above UL	Median (IQR)	Prevalence of inadequacy (%)	% of individuals above UL	Median (IQR)	Prevalence of inadequacy (%)	% of individuals above UL
Nutrients with AMDR
	Carbohydrate (% E)	67.5 (63.4‒70.8)	0.1	‒	68.6 (64.4‒71.4)	0.1	‒	68.4 (64.1‒71.4)	0.0	‒
	Protein (% E)	11.1 (10.2‒12.0)	20.0	‒	10.9 (10.4‒11.5)	13.2	‒	11.1 (10.4‒11.7)	12.4	‒
	Total fat (% E)	18.8 (15.7‒22.0)	61.2	‒	17.9 (15.3‒21.5)	66.2	‒	17.9 (15.2‒21.5)	67.0	‒
Nutrients with EAR
	Vitamin A (µg/1000 kcal)	251.02 (193.22‒320.78)	49.7	0.1	223.04 (177.92‒279.51)	45.4	0.0	226.63 (178.86‒280.56)	39.3	0.0
	Vitamin B1 (mg/1000 kcal)	0.41 (0.35‒0.49)	84.2	‒	0.43 (0.37‒0.48)	68.6	‒	0.43 (0.37‒0.51)	54.3	‒
	Vitamin B2 (mg/1000 kcal)	0.33 (0.27‒0.43)	88.6	‒	0.31 (0.26‒0.37)	91.0	‒	0.30 (0.26‒0.37)	88.5	‒
	Niacin (mg/1000 kcal)	10.16 (8.65‒11.93)	4.6	3.0	10.87 (8.81‒12.82)	0.6	10.5	10.63 (8.81‒12.87)	3.6	23.2
	Vitamin B6 (mg/1000 kcal)	1.15 (1.02‒1.31)	1.6	0.0	1.10 (0.99‒1.22)	0.2	0.0	1.16 (1.01‒1.31)	3.3	0.0
	Folate (µg/1000 kcal)	102.43 (84.46‒123.44)	99.3	0.0	93.72 (81.97‒109.19)	99.8	0.0	96.14 (84.46‒113.35)	99.6	0.0
	Vitamin B12 (µg/1000 kcal)	0.24 (0.13‒0.39)	97.8	‒	0.20 (0.12‒0.30)	99.5	‒	0.21 (0.12‒0.33)	98.8	‒
	Vitamin C (mg/1000 kcal)	41.33 (23.18‒66.87)	36.7	0.1	40.46 (22.56‒61.07)	32.7	0.0	41.89 (23.56‒62.62)	28.7	0.0
	Calcium (mg/1000 kcal)	249.98 (211.72‒298.78)	91.4	0.0	239.82 (211.75‒275.95)	86.9	0.0	243.19 (212.48‒283.66)	80.8	0.0
	Magnesium (mg/1000 kcal)	146.94 (119.17‒232.36)	39.2	44.4	131.74 (111.62‒196.58)	29.7	46.7	138.77 (115.02‒223.76)	18.8	57.0
	Phosphorus (mg/1000 kcal)	451.07 (405.72‒517.28)	1.1	0.0	437.61 (402.53‒489.11)	0.5	0.0	455.94 (411.48‒526.22)	1.1	0.0
	Iron (mg/1000 kcal)	6.09 (5.09‒7.29)	95.4	0.0	5.60 (4.86‒6.51)	96.5	0.0	5.85 (5.07‒6.82)	91.1	0.0
	Zinc (mg/1000 kcal)	3.65 (3.25‒4.26)	74.6	0.0	3.48 (3.20‒4.01)	61.6	0.0	3.61 (3.27‒4.34)	44.2	0.0
	Copper (mg/1000 kcal)	0.96 (0.87‒1.10)	0.1	0.0	0.90 (0.82‒1.01)	0.1	0.0	0.92 (0.84‒1.03)	0.6	0.0
Nutrients with AI
	Dietary fibre (g/1000 kcal)	16.80 (14.07‒19.86)	‒	‒	15.41 (13.07‒18.34)	‒	‒	15.97 (13.38‒18.98)	‒	‒
	Sodium (mg/1000 kcal)	1365.51 (1072.42‒1724.52)	‒	76.9a	1322.90 (1109.44‒1579.92)	91.6a	‒	1356.26 (1114.05‒1642.11)	‒	93.5a
	Potassium (mg/1000 kcal)	1049.78 (907.06‒1261.67)	‒	‒	942.21 (839.15‒1099.96)	‒	‒	982.69 (856.74‒1141.40)	‒	‒
	Manganese (mg/1000 kcal)	4.07 (3.38‒5.78)	‒	34.2	3.82 (3.34‒4.85)	37.8	‒	3.98 (3.37‒5.58)	‒	46.9

Note: The adequacy of energy‐adjusted dietary nutrient intake was assessed compared to the National Academies of Sciences, Engineering and Medicine's DRI recommendations [29].

Abbreviations: AI, adequate intake; AMDR, Acceptable macronutrient distribution range; EAR, estimated average requirement; IQR, interquartile range; UL, tolerable upper intake level.

^a Compared with the energy‐adjusted chronic disease risk reduction intake reference value.

4. Discussion

This nutritional survey, conducted as part of the S‐MaNGro cohort study, encompassed a nationally representative sample of 1999 pregnant women. The sample showed a reasonably balanced distribution across all residential areas and provided a better representation of the various ethnicities in Sri Lanka. The results suggest that most women may not be able to adequately meet their nutritional needs solely through their diet during pregnancy. Every country has its own food culture, which can profoundly affect national nutrition. A country's food culture typically shapes its population's dietary practices, eating patterns and social norms surrounding food. Additionally, the availability of certain food items in a country significantly influences its nutritional status. Sri Lanka, a multiethnic tropical island in South Asia, has rice as its staple food, typically served with spiced vegetable, fish, poultry or meat dishes, regardless of ethnic background or geographical location. While most Sinhalese follow Buddhism, which has no strict restrictions on meat consumption, some Buddhists strictly adhere to a vegetarian diet. Tamils, who predominantly follow Hinduism, abstain from consuming beef, whereas the Moor community avoids pork because of Islamic beliefs. Coastal areas feature a diet richer in fish and other seafood than poultry or meat. Sri Lanka's tropical environment provides an abundance of coconuts, fruits, vegetables, tubers and leafy greens, which are incorporated into various local dishes. Coconut milk is commonly used as a base for curries, while coconut oil is the preferred cooking oil [33].

However, the recent national economic crisis has severely impacted the daily lives of the entire population. A significant proportion of households resorted to food‐based coping strategies, either by skipping meals or reducing meal portions (24.2% vs. 46.2%) [19]. Particularly concerning is that households with pregnant and lactating women were more likely to adopt these coping strategies than were households without pregnant and lactating women (23.6% vs. 16.8%). The situation worsened as the economic crisis significantly affected national nutrition programmes [19]. Because the implementation of the current cohort study (August 2022–April 2024) coincided with Sri Lanka's severe economic crisis, it is reasonable to expect the financial crisis to have directly impacted the nutritional status of the cohort participants. In line with the World Food Programme's 2022 special report [19], our findings revealed that 24.6% of pregnant women in our cohort skipped at least one meal per day, most commonly breakfast and that the majority did not meet their nutritional requirements during pregnancy.

Globally, countries with the highest carbohydrate intake tend to be developing nations with low economic backgrounds, where people rely on low‐cost, starchy, carbohydrate‐heavy foods [34]. Sri Lanka's high dependence on carbohydrate‐rich foods may mirror this global context, while the nation's unique food culture also plays a significant role. The current study showed that pregnant women consumed only approximately 11% of total energy from protein, which is significantly lower than figures reported from developed countries [35] and aligns with findings from low‐ and middle‐income countries [36]. Globally, the primary sources of protein are plant‐based foods such as legumes and grains, followed by animal‐based foods like meat and dairy products [37]. Protein from animal‐based foods is considered to have greater nutritional value because it contains all essential amino acids. In Sri Lanka, plant protein largely contributes to protein intake [38], while the high cost of animal‐based foods, along with religious and cultural influences, limits the consumption of animal‐based protein. Our findings highlight significant disparities in nutritional intake, particularly among estate residents, who are among the most marginalized groups in Sri Lanka [39]. These communities face substantial barriers to accessing healthcare and nutrition, with estate sector living being a key determinant of maternal and child malnutrition [39]. Consistent with our findings, the World Food Programme's 2022 report highlighted the highest levels of food insecurity among estate sector residents [19].

BMI is a crucial indicator of nutritional status. The data from this cohort reflects a concerning trend observed in national statistics, with significant proportions of pregnant women categorized as either underweight or overweight/obese, underscoring the growing issue of body weight‐related health risks among reproductive‐aged women in Sri Lanka [18]. High dietary intake of saturated fatty acids has been associated with an increased risk of obesity [40]. Furthermore, the American Heart Association recommends that individuals consume only 5%–6% of their total energy from saturated fat to reduce the risk of heart disease associated with higher intakes of saturated fatty acids [41]. In Sri Lankan diets, coconut milk and coconut oil are the primary sources of saturated fatty acids [38]. In our cohort, the estimated median energy derived from saturated fatty acids exceeded 10% across all three trimesters, which could be one of the contributing factors to the high prevalence of overweight and obesity among Sri Lankan women.

Pregnancy requires additional vitamins and minerals to support maternal, placental and foetal interactions, ultimately promoting healthy pregnancy outcomes [6]. A national survey conducted in Sri Lanka in 2022 revealed the following nutrient deficiency rates among pregnant women, as determined by biomarkers: 11.0% for iron deficiency, 35.6% for vitamin D deficiency, 24.5% for zinc deficiency and 16.6% for vitamin B12 deficiency [42]. Our findings support these national trends, as a substantial proportion of pregnant women in our cohort had dietary intakes of several essential micronutrients, including vitamins B1, B2, B12, folate, calcium, iron and zinc, that were below the EARs. Consistent with our results, a systematic review of developing countries also reported suboptimal dietary intakes of folate, iron, zinc and calcium during pregnancy [36]. Additionally, a significant proportion of women in the current cohort exceeded the recommended sodium intake levels, suggesting a potential risk for chronic diseases. The maternal dietary patterns and nutritional deficiencies identified in the current study are consistent with those reported in other South Asian countries, such as India, Bangladesh and Nepal, where carbohydrate‐dominant diets and inadequate intakes of protein and essential micronutrients are prevalent [43]. A study from Bangladesh documented deficiencies in folate, vitamin B12 and calcium among reproductive‐aged women, which were linked to low socioeconomic status and low educational attainment [44]. Similarly, a recent systematic review from India identified deficiencies in folic acid, iron and vitamin B12, with iron deficiency being the most prevalent among pregnant women [45]. Moreover, a study in Nepal found a link between seasonal variability and maternal nutrition [46], highlighting the difficulties of maintaining consistent dietary adequacy throughout the year in countries where weather patterns and food availability vary across seasons. However, beyond the shared underlying factors observed in other South Asian countries, Sri Lanka faces unique challenges with the recent economic crisis by imposing additional food security and affordability barriers. This economic downturn has deepened the nutritional vulnerabilities of pregnant women, distinguishing Sri Lanka from its regional counterparts. These findings underscore the urgent need for region‐specific interventions tailored to each country's cultural and economic contexts to address maternal nutrition inadequacies effectively.

In 2016, the WHO published comprehensive antenatal care guidelines aimed at improving the quality of routine pregnancy care, with 14 of the 49 recommendations focused on nutrition during pregnancy. The WHO recommends 30–60 mg of oral elemental iron and 400 µg of folic acid daily for pregnant women in areas with a high prevalence of malnutrition. It also recommends daily supplementation of 1.5–2.0 g of oral elemental calcium for populations with low dietary calcium intake, alongside dietary advice [47]. Sri Lanka's national guidelines for prevention and control of maternal anaemia recommend 60 mg of elemental iron and 400 µg of folic acid daily, along with 100 mg of vitamin C, for all pregnant women from the beginning of the second trimester until 6 months postpartum [48]. Additionally, one tablet of folic acid tablet (1 mg) is recommended during the first trimester to prevent neural tube defects in newborns. The national maternal healthcare system provides these supplements free of charge to all pregnant women during pregnancy and for 6 months postpartum [48]. Although the current study did not assess maternal iron, folate and calcium supplement intake during pregnancy, a previous study in Sri Lanka [49] reported high compliance (80.1%) with the national antenatal supplementation programme. In light of this, our study's insufficient dietary micronutrient intake levels may significantly improve if full compliance with the antenatal iron‐folate supplementation programme is assumed. It is also important to note that the iron supplements provided through routine maternal care in Sri Lanka contain 2.2 times the recommended dietary allowance for iron in pregnancy (27 mg/day). Despite this, we found that 31.5% of pregnant women were anaemic in their third trimester. Therefore, it is crucial to investigate other possible causes of maternal anaemia beyond iron deficiency within the Sri Lankan context. Additionally, there is a need to re‐evaluate maternal practices related to the intake and storage of iron and folate supplements.

4.1. Implications

The implications of our findings extend beyond individual health outcomes, offering broader insights for developing national maternal nutrition plans and policies supported by evidence‐based data. Primary healthcare providers at the community level should take necessary actions to identify malnourished or at‐risk women during their first antenatal clinic visit and provide personalized care throughout pregnancy to improve their nutrition. Antenatal nutritional education programmes should focus on nutritional counselling, emphasizing balanced nutrition, dietary diversity and healthy eating practices. Additionally, these programmes should encourage limiting foods and nutrients associated with noncommunicable disease risks, such as sodium and saturated fatty acids. Addressing nutritional deficiencies requires carefully designed, customized programmes that integrate local food cultures tailored to the population's diverse ethnicities, religious beliefs and living environments. These programmes should also align with the capabilities and circumstances of individuals from varying socioeconomic backgrounds. Local healthcare providers are pivotal in guiding and personalizing these approaches, ensuring that recommendations are adapted to each individual's unique characteristics. This culturally and contextually sensitive approach can significantly enhance programme adherence and effectiveness, ensuring that interventions are both practical and impactful. Policymakers and administrators must also ensure access to adequate, affordable and good‐quality nutrition for pregnant women because prolonged malnutrition can have serious long‐term consequences for both maternal and child health if left unaddressed.

Additionally, scaling up initiatives such as food fortification and community‐based interventions aimed at enhancing dietary diversity has the potential to mitigate widespread nutrient deficiencies nationwide. We recommend continuing and strengthening the national maternal supplementation programme to mitigate the impact of inadequate dietary nutrient intake. Collaborative efforts with global organizations like WHO and UNICEF could strengthen national maternal health programmes by providing technical support, funding and capacity building, thereby ensuring sustainable maternal and child nutrition improvements. These approaches, combined with continuous monitoring and evaluation of implemented strategies, are essential for achieving long‐term health benefits and addressing the broader public health challenges of maternal malnutrition.

While these recommendations and interventions are essential for addressing maternal malnutrition at the national level, it is equally important to situate these findings within the global context. Maternal malnutrition remains a pressing public health concern worldwide, with significant disparities in nutritional status and health outcomes between low‐ and middle‐income countries and their high‐income counterparts. In low‐ and middle‐income countries, dietary deficiencies, especially protein and micronutrients, are common due to socioeconomic inequalities. In contrast, high‐income countries have effectively implemented nutritional programmes to combat maternal malnutrition, such as food fortification. Thus, addressing the disparities in maternal nutrition between low‐ and high‐income contexts is critical for achieving the Sustainable Developmental Goals (SDGs), particularly SDG 2 (Zero Hunger) and SDG 3 (Good Health and Well‐being). The findings from the current cohort will significantly contribute to the global discussion on maternal nutrition, emphasizing the unique challenges South Asia faces, particularly those specific to Sri Lanka amidst the economic crisis.

4.2. Strengths and Limitations

Our study has several strengths. To the best of our knowledge, this is the first study in Sri Lanka to prospectively assess pregnant women's dietary intake and the adequacy of nutrients in a large nationwide sample. We employed a validated FFQ for dietary assessment at three different points during pregnancy, which may help capture seasonal variations in food availability. The use of research staff with specific language skills optimized communication with participants during data collection. Additionally, we used BMI and haemoglobin data measured at antenatal clinics as biomarkers to assess the nutritional status of the study population. Finally, we identified and excluded implausible energy intake data (both under‐ and over‐reporting) before the final analysis, improving the accuracy of our findings. Despite these strengths, our study has certain limitations. Variations in growing conditions, storage methods, storage durations and cooking practices may have affected the nutrient content of the foods consumed, which we did not account for in this study. Furthermore, the FFQ data may have been subject to recall bias and potential inaccuracies in reporting nutritional information. However, to mitigate the impact of potential under‐reporting, we used energy‐adjusted nutrient values rather than crude intake figures.

5. Conclusion

The study findings indicated imbalances in macronutrient distribution, and deficiencies in multiple micronutrients in pregnant women's diets, with economically disadvantaged groups being the most affected. These results emphasize the urgent need for immediate measures to improve maternal nutrition in the Sri Lankan context.

Author Contributions

Conceptualization: Malshani Lakshika Pathirathna, Megumi Haruna and Satoshi Sasaki. Methodology: Malshani Lakshika Pathirathna, Megumi Haruna, Satoshi Sasaki and Yasuhiro Hagiwara. Software modification: Malshani Lakshika Pathirathna, Megumi Haruna and Satoshi Sasaki. Investigation: Malshani Lakshika Pathirathna and Megumi Haruna. Resources, Megumi Haruna. Data curation, Malshani Lakshika Pathirathna. Formal analysis: Malshani Lakshika Pathirathna and Yasuhiro Hagiwara. Writing–original draft: Malshani Lakshika Pathirathna. Writing–review and editing: Malshani Lakshika Pathirathna, Megumi Haruna, Kaori Yonezawa, Yuriko Usui and Yasuhiro Hagiwara. Visualization: Malshani Lakshika Pathirathna, Megumi Haruna, Satoshi Sasaki, Kaori Yonezawa and Yuriko Usui. Supervision: Megumi Haruna and Satoshi Sasaki. Project administration: Malshani Lakshika Pathirathna and Megumi Haruna. Funding acquisition: Megumi Haruna and Malshani Lakshika Pathirathna. All authors have read and agreed to the published version of the manuscript.

Ethics Statement

The study was approved by the Ethics Review Committee of the Faculty of Medicine, University of Peradeniya, Sri Lanka (2021/EC/90), and by the Ethics Review Committee of the Graduate School of Medicine, University of Tokyo, Japan (2022311NI). The study was conducted in accordance with the Declaration of Helsinki, and Informed consent was obtained from all subjects involved in the study.

Conflicts of Interest

The authors declare no conflicts of interest.

Transparent Peer Review

The peer review history for this article is available at https://www.webofscience.com/api/gateway/wos/peer-review/10.1111/jhn.70020.

1.

See Table A1.

Table A1.

Dietary Reference Intake for pregnant women aged 14–50 years: (Presented only the selected reference values utilized in this study).

Nutrient		DRIs during pregnancy (14‒50 years)
		AMDR	EAR	AI	UL
Nutrients with AMDR
	Carbohydrate (% E)	45‒65	‒	‒	‒
	Protein (% E)	10‒30a, 10‒35b	‒	‒	‒
	Total fat (% E)	25‒35a, 20‒35b	‒	‒	‒
Nutrients with EAR
	Vitamin A (µg/day)	‒	530a, 550b	‒	2800a, 3000b
	Vitamin B1 (mg/day)	‒	1.2	‒	‒
	Vitamin B2 (mg/day)	‒	1.2	‒	‒
	Niacin (mg/day)	‒	14	‒	30a, 35b
	Vitamin B6 (mg/day)	‒	1.6	‒	80a, 100b
	Folate (µg /d)	‒	520	‒	800a, 1000b
	Vitamin B12 (µg/day)	‒	2.2	‒	‒
	Vitamin C (mg/day)	‒	66a, 70b	‒	1800a, 2000b
	Calcium (mg/day)	‒	1000a, 800b	‒	3000a, 2500b
	Magnesium (mg/day)	‒	335a, 290c, 300d	‒	350
	Phosphorus (mg/day)	‒	1055a, 580b	‒	3500
	Iron (mg/day)	‒	23a, 22b	‒	45
	Zinc (mg/day)	‒	10.5a, 9.5b	‒	34a, 40b
	Copper (mg/day)	‒	0.785a, 0.800b	‒	8a, 10b
Nutrients with AI
	Dietary fibre (g/day)	‒	‒	28	‒
	Sodium (mg/day)	‒	‒	1500	2300e
	Potassium (mg/day)	‒	‒	2600a, 2900b	‒
	Manganese (mg/day)	‒	‒	2	9a, 11b

Note: National Academies of Sciences, Engineering and Medicine's DRI recommendations [29].

Abbreviations: AI, adequate intake; AMDR, Acceptable macronutrient distribution range; EAR, estimated average requirement; UL, tolerable upper intake level.

^a 14‒18 years.

^b 19‒50 years.

^c 19‒30 years.

^d 31‒50 years.

^e Chronic disease risk reduction intake.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.