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. 2025 Nov 29;22(1):e70146. doi: 10.1111/mcn.70146

Maternal Diet Quality and Third Trimester Depression: Insights From a Nepali Birth Cohort Study

Kalpana Chaudhary 1,2, Shaun Ranade 3,4, Pratiksha Paudel 1,5, Bhawana Shrestha 5, Shija Panjiyar 1, Shraddha Thapaliya 5, Reena Koju 5, Abha Shrestha 6, Prasanna Rai 1,5, Bandana Paneru 1,5, Namuna Shrestha 5, Archana Shrestha 1,2,7, Shristi Rawal 8,✉
PMCID: PMC12663694  PMID: 41316875

ABSTRACT

Maternal diet quality and perinatal depression significantly impact maternal and child health, yet their relationship remains underexplored in low‐resource settings. This cross‐sectional study examined the association between overall diet quality and risk of depression during the third trimester among 296 pregnant women receiving antenatal care at Dhulikhel Hospital, Nepal (August 2023–January 2024). Depression risk was assessed using the Edinburgh Postnatal Depression Scale (EPDS), with scores ≥ 12 indicating elevated symptoms. Diet quality was measured using an adapted Nepali version of the 23‐item PrimeScreen questionnaire, generating a Prime Diet Quality Score (PDQS) ranging from 0 to 46. Multivariable logistic regression models were used to estimate the association between PDQS and depression risk, adjusting for age, education, ethnicity, occupation, parity, gestational week, physical activity, and pre‐pregnancy BMI. The mean PDQS was 24.7 (SD = 3.1), and 22.3% of participants screened positive for depression. Each 1‐point increase in PDQS was associated with 16% lower odds of depression (adjusted OR: 0.84; 95% CI: 0.70–0.90; p = 0.002). These findings suggest that higher overall diet quality is associated with a reduced likelihood of third trimester depression. Further longitudinal studies are warranted to assess causality and inform targeted nutritional interventions. If supported by further studies, incorporating brief dietary assessments like PrimeScreen into antenatal care may potentially offer a feasible strategy to identify women with suboptimal diet quality and co‐occurring depressive symptoms in low‐ and middle‐income countries.

Keywords: diet quality, maternal health, maternal nutrition, mental health, perinatal depression, pregnancy

Summary

  • 22.3% of third trimester pregnant women showed elevated depressive symptoms (EPDS  ≥  12). Each 1‐point increase in overall diet quality (PDQS) was linked to a 16% reduction in the odds of depression.

  • Higher intake of whole fruits, vegetable oil, and refined grains was associated with a lower risk of depression, while frequent consumption of processed meats, sweets, fried foods, sweetened drinks, fish, beans/pulses, and whole grains was linked to a higher risk.

  • Our findings highlight the importance of incorporating nutritional assessment and education into routine antenatal care as a potentially practical approach to identify and support women at risk for poor diet quality and depressive symptoms.

1. Introduction

Depression during pregnancy is a major public health concern, affecting approximately one in ten women globally, with even higher rates reported in low‐ and middle‐income countries (LMICs) (Araujo et al. 2010; Grigoriadis et al. 2013; Grote et al. 2010). Prenatal depression is not only common but also clinically significant, given its association with adverse outcomes for both mother and child. Studies show that depression during pregnancy is associated with low birthweight, preterm birth, intrauterine growth restriction, and pregnancy complications (Araujo et al. 2010; Grigoriadis et al. 2013; Grote et al. 2010). Beyond pregnancy, depression during this critical period is linked to long‐term emotional, social, and cognitive challenges in the offspring, such as impaired academic performance, malnutrition, respiratory illness, and increased risk of future mental health disorders (Alder et al. 2007; Aryal et al. 2018; Davalos et al. 2012; Dunkel Schetter and Tanner 2012; Gentile 2017; Szegda et al. 2014). Identifying and addressing modifiable risk factors for prenatal depression is essential to prevent both immediate and long‐term adverse outcomes for both the mother and child. While interventions such as mental health counselling and social support have demonstrated benefit, there is growing recognition of maternal nutrition as a potentially modifiable and underexplored factor contributing to perinatal mental health (O'Connor et al. 2019; US Curry et al. 2019).

Maternal diet quality plays a crucial role in supporting fetal development and reducing complications during pregnancy (Philipps and Johnson 1977; Y.‐L. Wang et al. 2024). Emerging evidence also suggests that dietary patterns may influence maternal mental health, including the risk of prenatal depression. Although systematic reviews point to a possible link between poor diet quality and depressive symptoms, findings remain inconsistent, and most studies have been conducted in high‐income settings (Baskin et al. 2015; Jiang et al. 2018; Philipps and Johnson 1977). In lower‐ and middle‐income countries (LMICs), where food insecurity, micronutrient deficiencies, and limited access to diverse, nutrient‐rich foods are more prevalent, the relationship between diet and mental health may be even more pronounced and important to study (Sarlio‐Lähteenkorva and Lahelma 2001; P. Wang et al. 2023). Food insecurity and financial strain in these low‐resource settings can simultaneously affect dietary intake and psychological well‐being (Choudhury et al. 2025; Firth et al. 2020), making it difficult to disentangle whether poor diet contributes to depressive symptoms or whether pre‐existing depression leads to unhealthy eating behaviours (Madeghe et al. 2022). Depression can reduce motivation to eat healthy, disrupt appetite regulation, and increase cravings for energy‐dense comfort foods (Li et al. 2017), further complicating this bidirectional relationship. Moreover, shared contextual factors such as poverty, social stress, and limited social support may independently influence both dietary quality and mental health, creating overlapping pathways of risk that are difficult to separate empirically. While behavioural pathways suggest that depression influences diet (Marx et al. 2021), biological mechanisms strongly link diet quality to depression through increased systemic inflammation and altered gut microbiota, particularly in diets high in processed and sugary foods (Cardinal et al. 2021; Wang et al. 2024). Given these interrelated pathways, it remains challenging to determine directionality; however, examining overall diet quality provides an important entry point for understanding modifiable factors that could support maternal mental well‐being. While prior studies have examined individual nutrients, evaluating overall diet quality offers a more holistic and practical approach. Diets rich in fruits, vegetables, whole grains, and healthy fats are thought to have anti‐inflammatory properties and may help reduce depressive symptoms. This approach is particularly relevant for identifying accessible and low‐cost strategies to support maternal well‐being in resource‐constrained settings (P. Wang et al. 2023).

This study explores the association between overall diet quality and risk of depression during pregnancy among women in Nepal, an LMIC with high rates of nutritional deficiencies and unmet mental health needs. We used the PrimeScreen questionnaire, a validated and brief dietary screener designed to assess overall dietary patterns. By classifying food groups into “healthy” and “unhealthy” categories, PrimeScreen enables the rapid assessment of diet quality with strong reproducibility and validity (Cano‐Ibáñez et al. 2022; Rifas‐Shiman et al. 2001). Its brief format makes it particularly suitable for large‐scale or time‐sensitive antenatal care settings. In this context, we examined whether higher overall diet quality is associated with lower risk of prenatal depression among third trimester pregnant women in a hospital‐based cohort in Nepal.

2. Methods

2.1. Study Design

We conducted a cross‐sectional analysis using data from the ongoing Dhulikhel Hospital Birth Cohort Study. This prospective hospital‐based cohort study assesses pre‐pregnancy, pregnancy, and post‐partum risk factors and their association with maternal and child health outcomes through multiple follow‐up periods during pregnancy.

2.2. Study Site

We recruited pregnant women during their scheduled antenatal care visits at Dhulikhel Hospital and Kathmandu University Hospital in Nepal. Dhulikhel Hospital has a catchment population of approximately 1.9 million people and delivers around 3000–3500 babies annually.

2.3. Study Participants and Recruitment

Pregnant women attending the OB‐GYN Outpatient Department (OPD) at Dhulikhel Hospital for antenatal care were recruited into the study. Trained research assistants (RAs) led the recruitment and screening efforts in the OPD, with the help of health providers, and obtained written informed consent from eligible participants. Enrolment took place during participants' initial antenatal visit or at their first encounter with the study team, most often in the first or second trimester, followed by subsequent assessments during later trimesters and at 6 weeks postpartum. Eligible participants were first approached in the waiting area and invited to learn more about the study. Women who expressed interest were then escorted to a private room, where the research team obtained consent, completed enrolment procedures and conducted interviews. As part of a nested sub‐study, we specifically and additionally collected data on depressive symptoms and dietary intake from enrolled women in their third trimester. This period was selected to capture a time when physiological and psychosocial stressors are heightened and dietary patterns tend to be more stable.

2.4. Eligibility Criteria

The inclusion criteria required that participants be pregnant women of all ages who either had a confirmed pregnancy through ultrasound and scheduled ANC visits at Dhulikhel Hospital or received maternal services at any stage of their pregnancy. Women were also included regardless of the number of fetuses or the presence of high‐risk pregnancy conditions.

Pregnant women were excluded if, in the judgment of RAs, they had apparent communication difficulties (such as hearing, speech, or cognitive impairments), were unable to understand the Nepali language, required hospital admission due to severe illness, or declined to provide informed consent to participate.

2.5. Data Collection Method(s)

We administered structured questionnaires at enrolment and during each follow‐up visit to collect information on various sociodemographic, lifestyle, and clinical characteristics. In addition, we abstracted data from medical record reviews. For example, we collected information on maternal age, alcohol use before pregnancy, education level, employment status, ethnicity, marital status, smoking during or before pregnancy, pre‐pregnancy depression, height, pre‐pregnancy diabetes, nausea or vomiting during pregnancy, pre‐pregnancy BMI, and number of prior births through interview, while birth outcomes and maternal weight during pregnancy were abstracted from medical records abstraction.

The number of follow‐up visits varied by gestational age at enrolment. Women enrolled in the first trimester completed three follow‐up visits (one in each subsequent trimester and one at 6 weeks postpartum), whereas those enrolled in the second trimester completed two visits, one in the third trimester and one at 6 weeks postpartum. Data collection was designed to align with the physiological and psychosocial changes that occur throughout pregnancy and the postpartum period, with each trimester focusing on distinct domains of maternal health. At the 6‐week postpartum visit, data were collected on maternal recovery, breastfeeding, postnatal health, and infant outcomes. Not all instruments were administered at every visit; questionnaires were tailored to each stage to capture the most relevant information and provide a comprehensive understanding of maternal health trajectories across the perinatal continuum. As part of this nested cross‐sectional sub‐study within the longitudinal birth cohort, we additionally collected concurrent data on depressive symptoms and dietary intake from enrolled women in their third trimester.

2.6. Data Collection Tools

2.6.1. Prenatal Depression

Risk of depression was assessed as the primary outcome in the third trimester. We utilised the Edinburgh Postnatal Depression Scale (EPDS), a 10‐item self‐reported tool, to evaluate depressive symptoms in pregnant women. It is specifically designed to assess depressive symptoms during pregnancy and the postpartum period. It consists of 10 short statements, each with four possible responses that women record based on their feelings over the past week (Cox et al. 1987). The mothers' responses are scored as 0, 1, 2, or 3, depending on the severity of the symptoms. Items 3 and 5 to 10 are reverse scored (i.e., 3, 2, 1, and 0). The total score is calculated by summing the scores for all 10 items. Our study utilised the validated Nepali version of the EPDS, demonstrating strong internal consistency with a Cronbach's alpha value of 0.74, thereby ensuring its reliability for assessing prenatal depression (Bhusal et al. 2016). Further, we dichotomised the depression score, classifying scores above 12 as indicating risk of depression (Bhusal et al. 2016).

2.6.2. Diet Quality

PrimeScreen, a brief 21‐item food frequency questionnaire designed for use in both pregnant and nonpregnant populations, was administered in the third trimester to assess the diet quality of pregnant women in our sample (Rifas‐Shiman et al. 2001). As previously described, we translated, adapted and validated the PrimeScreen questionnaire for use in Nepalese pregnant women (Martin et al. 2022). The modified version (with two added items) proved to be a reliable and valid tool for assessing dietary intake in our pregnant population in Nepal. It evaluates the consumption frequency of 23 food group components (score range: 0–46), categorised into healthy (13 groups) and unhealthy (10 groups) components, with higher scores indicating better diet quality. Similar to other studies, higher scores for increased consumption of healthy food groups (e.g., fruits, vegetables, whole grains) indicate better diet quality, where 0–1 servings/week = 0 points, 2‐3 servings/week = 1 point, and 4+ servings/week = 2 points. Higher scores reflect lower consumption of unhealthy food groups (e.g., red meat, processed meat, sugar‐sweetened beverages) (Gicevic et al. n.d.; T Teresa et al. 2018).

2.6.3. Socio‐Demographic and Reproductive Variables

We administered a structured questionnaire at enrolment to gather information on various sociodemographic, lifestyle, and obstetric characteristics. We collected details on maternal age (continuous), education level (illiterate, primary, secondary and higher secondary and above), religion (Hindu, Buddhism and Others), family type (nuclear, joint), occupation (service, self‐employed, housewife, others), ethnicity (Brahmin/Chhetri, Janjati, Newar, Dalit/Madhesi/Others), pre‐pregnancy BMI, number of prior births, gestational week, and number of ANC visits.

2.6.4. Pre‐Pregnancy Body Mass Index (BMI)

We abstracted weight and height measurements from the ANC card and used self‐reported pre‐pregnancy weight to calculate the pre‐pregnancy BMI. While we had weight measurements available from the first trimester for many participants, not all women initiated antenatal care during this period. Therefore, to ensure consistency and completeness, we used only self‐reported pre‐pregnancy weight to compute pre‐pregnancy BMI.

2.6.5. Physical Activity

We used the 7‐item International Physical Activity Questionnaire (IPAQ) to assess individuals' physical activity over the previous 7 days. This tool assesses the intensity and types of physical activity, as well as the time spent sitting, to estimate total physical activity in MET minutes per week. Vigorous physical activity included construction work, carrying heavy loads like bricks, running, and hiking, whereas moderate activities encompassed farm work, drawing water, carrying lighter loads, swimming, and yoga (Algallai et al. 2023). The frequency and intensity of physical activity are recorded in terms of days and minutes. METs (Metabolic Equivalents) are used to quantify the intensity of physical activities and to analyse the IPAQ data. We categorised the METS score into [i] inactive, [ii] minimally active (at least 600 MET‐min/week), [iii] HEPA active (health‐enhancing physical activity; a high active category (at least 3000 MET‐minutes/week))(IPAQ ‐ Score, n.d.). This instrument is suitable for national population‐based prevalence studies of participation in physical activity (Lee et al. 2011).

2.7. Ethical Consideration

We received ethical approval from the ethical/institutional review boards of Kathmandu University (KUIRC Approval number: 10/23). We verbally explained the study to participants before obtaining the written informed consent. During the consent process, we informed participants that their participation was entirely voluntary and that they could withdraw from the study at any time. We also emphasised that their decision to participate would not impact the quality of their treatment and care at the clinic. We obtained written informed consent from those who agreed to participate in the study.

2.8. Data Analysis

We analysed all data using STATA 18.0 software (STATA Corporation, College Station, TX, USA). We presented quantitative data as frequencies and percentages for categorical variables, while we expressed continuous variables as means and standard deviations. To examine the associations between diet quality and depression risk, we utilised logistic regression models. We adjusted for potential confounding variables, including age, education, ethnicity, occupation, parity, gestational week, physical activity, and pre‐pregnancy BMI (Baskin et al. 2015; Cano‐Ibáñez et al. 2022; Chalise et al. 2022; Khan et al. 2020; Y.‐L. Wang et al. 2024; Yin et al. 2021). We reported odds ratios (OR) with 95% confidence intervals (CI) to evaluate the strength and significance of the associations. We set statistical significance at p‐value < 0.05 for all analyses. In the unadjusted (bivariate) logistic regression, we analysed the association between each food group and depression status (yes/no) as the outcome. For the multivariable analysis, we performed logistic regression for each food group with depression status as the outcome, adjusting for overall diet score, age, education, ethnicity, occupation, gestational week, physical activity, and pre‐pregnancy BMI.

3. Results

Between August 2023 and January 2024, we enrolled 296 participants in their third trimester of pregnancy.

3.1. Participant Characteristics

Table 1 presents the socio‐demographic characteristics of the study participants. The average age was 27.2 years (SD = 3.6), with over half completing high school education. Approximately 36.8% of participants identified as belonging to the Brahmin/Chhetri ethnicity. Most practised Hinduism (80.4%), while 15.9% identified as Buddhists. A similar proportion of participants were homemakers (56.1%) and lived in joint families (55.4%). Around 30% were overweight, and 15% had obesity before pregnancy. The average prime diet quality score (PDQS) was 24.7 (SD = 3.1). Most participants (93.9%) were minimally physically active.

Table 1.

Socio‐demographic characteristics of the participants, n = 296.

Characteristics Mean (SD) or n (%)
Age in years ± SD 27.2 ± 3.6
Education
Illiterate 4 (1.4)
Primary (1–8 years) 54 (18.2)
Secondary level (9–12 years) 166 (56.1)
Higher secondary and above (13 years and above) 72 (24.3)
Ethnicity
Brahmin/Chhetri 109 (36.8)
Janjati 88 (29.7)
Newar 73 (24.7)
Madhesi/dalit/others 26 (8.8)
Religion
Hindu 238 (80.4)
Buddhism 47 (15.9)
Others 11 (3.7)
Occupation
Employed 67 (22.6)
Self‐employed 37 (12.5)
Housewife 166 (56.1)
Agriculture/others 26 (8.8)
Annual personal income in $, median (IQR) 18181.8 (25454.5)
Annual family income in $, median (IQR) 45454.5 (40909.0)
Family type
Nuclear family 132 (44.6)
Joint Family 164 (55.4)
Household size, mean ± SD 5.2 ± 2.8
Pre‐pregnancy BMI
Underweight (Below 18.5) 8 (2.7)
Normal (18.5–24.99) 154 (52.0)
Overweight (25.0–29.99) 89 (30.1)
Obese (30.0 and above) 45 (15.2)
Parity
Nulli para 134 (45.6)
Primi para 130 (44.2)
Multipara 30 (10.2)
Gestational week, mean ± SD 33.1 ± 3.8
Number of ANC visits, mean ± SD 6.4 ± 2.8
PDQS score 24.7 ± 3.1
Physical activity
Inactive 18 (6.1)
Minimally physically active 278 (93.9)
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Note: $ = 132 rupees in Nepalese currency, minimally active physical activity = at least 600 MET‐min/week

PDQS = PrimeScreen Diet Quality Score, SD = standard deviation, maximum PDQS is 42, with higher scores indicating higher diet quality.

3.2. Prenatal Depression

Table 2 shows the prevalence of depressive symptoms among women in their third trimester, as measured by the EPDS scale. The overall prevalence of depressive symptoms was 22.3%, and the overall depression score was 7.2 ± 5.6.

Table 2.

Responses to the Edinburgh postnatal depression scale (EPDS) and prevalence of depressive symptoms among participants in their third trimester in the Dhulilkhel birth cohort study.

Characteristics Mean ± SD
I have been able to laugh and see the funny side of things 0.4 ± 0.6
I have looked forward with enjoyment to things 0.4 ± 0.6
I have blamed myself unnecessarily when things went wrong 0.8 ± 0.9
I have been anxious or worried for no good reason 0.9 ± 1.0
I have felt scared or panicky for no very good reason 0.9 ± 1.0
Things have been getting on top of me 0.9 ± 1.0
I have been so unhappy that I have had difficulty sleeping 0.7 ± 0.9
I have felt sad or miserable 1.0 ± 0.9
I have been so unhappy that I have been crying 0.5 ± 0.7
The thought of harming myself has occurred to me 0.3 ± 0.6
Overall EPDS mean score 7.2 ± 5.6
No or minimal depressive symptoms 227 (77.7)
Possible depression (EPDS score above cutoff): 65 (22.2)
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Note: EPDS items are scored on a 4‐point Likert scale (0–3), with higher scores indicating more severe depressive symptoms. The total score ranges from 0 to 30. A sum score greater than 12 was used to indicate probable prenatal depression.

3.3. Association Between Overall Diet Quality Score and Risk of Depression

Table 3 demonstrates a significant association between depression risk and diet quality score. In the unadjusted logistic regression analysis, diet quality was significantly associated with risk of depression (cOR = 0.83, 95% CI: 0.7,0.9; p = < 0.0001). This association remained significant in the multivariable model (p = 0.002). After adjusting for age, education, ethnicity, occupation, parity, and week of gestation, an inverse association was observed between overall diet quality and depression risk in the 3rd trimester. Each unit increase in the PDQS was associated with a 16% reduction in the odds of having depression risk (adjusted OR: 0.84, 95% CI: 0.76–0.93, p < 0.001).

Table 3.

Association between overall diet quality and odds of elevated depression risk (Edinburgh postnatal depression scale ≥ 12) in the third trimester: Dhulikhel birth cohort study.

Univariate Multivariate
Elevated depressive symptoms: (Edinburgh postnatal depression scale score ≥ 12) cOR 95% CI p‐value aOR 95% CI p‐value
PDQS score (continuous) 0.83 0.7, 0.9 < 0.0001 0.84 0.7, 0.9 0.002
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Note: Adjusting for age, education, ethnicity, occupation, parity, gestational week, physical activity, and pre‐pregnancy BMI.

Abbreviations: cOR = crude odds ratio, aOR = adjusted odds ratio.

3.4. Association Between Individual Food Groups and Risk of Depression

Table 4 illustrates observed associations between individual food groups in the PrimeScreen and risk of depression. Unadjusted logistic regression analysis revealed a negative and significant association between depression risk and several food groups, including other vegetables, citrus fruits, other whole fruits, egg, and vegetable oil. In the multivariable logistic regression models adjusting for overall diet quality, age, education, ethnicity, occupation, gestational week, physical activity, and pre‐pregnancy BMI, significant associations only persisted for other whole fruits, vegetable oil and refined grains, with higher consumption of these food groups associated with lower depressive symptoms. In the unadjusted models, positive and significant associations were observed with other food groups, including beans and pulses, fish, red meat, processed meat, baked products, soft drinks, fried food, desserts and packaged foods. In multi‐variable models, positive associations remained significant for beans & pulses, fish, whole grains, processed meat, baked products, fried foods and desserts with higher intake associated with higher risk of depressive symptoms, even after adjusting for overall diet quality score, age, education, ethnicity, occupation, gestational week, physical activity, and pre‐pregnancy BMI.

Table 4.

Associations between individual food group consumption (PrimeScreen) and risk of elevated depressive symptoms (Edinburgh postnatal depression scale ≥ 12) in the third trimester of pregnancy: Dhulikhel birth cohort study (n = 290).

Univariate Multivariate
Elevated depressive symptoms: (Edinburgh postnatal depression scale score ≥ 12) OR 95% CI p‐value aOR 95% CI p‐value
Green leafy vegetables
0–1 servings Ref Ref
2–3 servings 0.47 0.1, 5.5 0.55 0.47 0.1, 6.3 0.57
4 or more servings 0.64 0.0, 7.3 0.72 0.76 0.1, 10.1 0.83
Cruciferous vegetables
0–1 servings Ref Ref
2 or more servings 1.7 0.8, 3.7 0.16 2.3 0.9, 5.5 0.05
Carrots
0–1 servings Ref Ref
2 or more servings 1.4 0.8, 2.5 0.21 1.9 0.9, 3.7 0.05
Other vegetables
0–3 servings Ref Ref
More than 4 servings 0.45 0.2, 0.8 0.02 0.60 0.2, 1.2 0.17
Citrus fruits
0–3 servings Ref Ref
4 or more servings 0.43 0.2, 0.8 0.01 0.54 0.2, 1.1 0.11
Other whole fruits
0–3 servings Ref Ref
4 or more servings 0.26 0.1, 0.4 < 0.0001 0.30 0.1, 0.6 0.001
Beans, pulses
0–3 servings Ref Ref
4 or more servings 1.9 1.1, 3.3 0.02 2.1 1.1, 3.9 0.01
Nuts
0–3 servings Ref Ref
4 or more servings 1 0.5, 1.7 0.98 0.94 0.5, 1.7 0.84
Chicken
0–1 servings Ref Ref
2 or more servings 1.5 0.7, 3.1 0.22 1.8 0.8, 3.9 0.11
Fish
0–1 servings Ref Ref
2 or more servings 2.7 1.5, 4.8 < 0.0001 2.7 1.4, 5.2 0.001
Egg
0–3 servings Ref Ref
4 or more servings 0.40 0.2, 0.7 0.004 0.57 0.2, 1.1 0.12
Whole grains
0–1 servings Ref Ref
2 or more servings 5.4 2.9, 10.1 < 0.0001 5.8 2.9, 11.3 < 0.0001
Vegetable oil
0–3 servings Ref Ref
4 or more servings 0.45 0.2, 0.9 0.03 0.28 0.1, 0.6 0.003
Red meat
0–1 servings Ref Ref
2 or more servings 2.0 1.1, 3.7 0.01 1.4 0.7, 2.7 0.23
Potatoes
0–3 servings Ref Ref
4 or more servings 0.71 0.3, 1.2 0.25 0.56 0.2, 1.1 0.09
Processed meat
0–3 servings Ref Ref
4 or more servings 3.6 2.1, 6.5 < 0.0001 3.5 1.8, 6.7 < 0.0001
Dairy products
0–3 servings Ref Ref
4 or more servings 1.3 0.7, 2.3 0.36 1.1 0.5, 2.2 0.64
Refined grains
0–3 servings Ref Ref
4 or more servings 0.61 0.3, 1.2 0.16 0.39 0.1, 0.8 0.02
Baked products
0–1 servings Ref Ref
2 or more servings 3.1 1.6, 5.5 < 0.0001 2.4 1.2, 4.6 0.01
Soft drinks
0–1 servings Ref Ref
2 or more servings 5.9 3.2, 10.9 < 0.0001 5.1 2.5, 10.1 < 0.0001
Fried foods
0–3 servings Ref Ref
4 or more servings 5.1 2.7, 9.2 < 0.0001 4.1 2.1, 8.1 < 0.0001
Desserts
0–1 servings Ref Ref
2 or more servings 4.6 2.5, 8.5 < 0.0001 3.7 1.9, 7.2 < 0.0001
Packaged foods
0–1 servings Ref Ref
2 or more servings 1.9 1.1, 3.6 0.02 1.2 0.6, 2.6 0.47
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Note: Food groups having fewer numbers in one group collapsed with the other group. p‐value in bold represents statistical significance.

Adjusting for overall diet quality score, age, education, ethnicity, occupation, gestational week, physical activity, and pre‐pregnancy BMI.

4. Discussion

The relationship between maternal diet quality and perinatal depression is an emerging area of interest, particularly in LMICs where nutritional deficiencies and mental health disparities often coexist (Madeghe et al. 2022). This study examined the association between maternal diet and depression symptoms during the third trimester among pregnant women at Dhulikhel Hospital, Kathmandu University Hospital, Nepal. After adjusting for age, education, ethnicity, occupation, parity, and week of gestation, an inverse association was observed between overall diet quality and depression risk in the 3rd trimester. Specifically, each unit increase in diet quality score as assessed by PDQS was associated with a 16% reduction in the odds of experiencing depressive symptoms.

In low and middle‐income countries like Nepal, the reported rates of depression are higher than the global average, yet screening for mental health is absent in prenatal settings (Pengpid et al. 2025). The prevalence of women at risk of depression in our sample (22.3%) is consistent with findings from prior community‐based studies conducted in Nepal, which reported prenatal depression rates ranging from 23.8% to 24.8%. However, it is slightly higher than a hospital‐based study, which reported a prevalence of 18% (Chalise et al. 2022; Mahendran et al. 2020). This slightly higher prevalence in our study may reflect our focus specifically on the third trimester, a period associated with heightened vulnerability to depression (Bisetegn et al. 2016). Compared to other LMICs, our observed prevalence was lower than in Ethiopia (24.94%) (Biratu and Haile 2015), South Africa (39%) (Hartley et al. 2011), Tanzania (39.5%) (Kaaya et al. 2010), but higher than in Brazil (11.4%) (Faisal‐Cury et al. 2021). A systematic review and meta‐analysis reported pooled antenatal depression prevalence rates of 17.7% in India and 15.87% in Sri Lanka (Mahendran et al. 2020). Higher rates were reported in Pakistan (32.2%) and Nepal (50%) (Mahendran et al. 2020). Differences in prevalence estimates across settings may reflect variations in sociodemographic and cultural factors, as well as methodological differences in assessment tools and cut‐off thresholds. For example, our study utilised the EPDS with a cut‐off score of more than 12, whereas other studies used thresholds of 13 or 14 (Hartley et al. 2011) or alternative tools, such as the PHQ‐9 or the Kiswahili version of the Hopkins Symptoms Checklist (Acheanpong et al. 2022; Katon et al. 2011).

We observed a significant inverse association between overall diet quality, as measured by the PDQS, and risk of depression in the third trimester. These findings suggest that higher‐quality diets may potentially play a protective role against antenatal depression. However, given the cross‐sectional design, this interpretation should be viewed as one possible explanation rather than a definitive causal relationship. The biological plausibility of the diet–depression link is supported by evidence that poor diets, which are high in sugars and saturated fats, promote systemic inflammation, while deficiencies in essential nutrients like omega‐3 fatty acids, vitamins B6, B12, and D, are associated with depressive symptoms(Acheanpong et al. 2022; Cox et al. 1987; Sarlio‐Lähteenkorva and Lahelma 2001). In addition to overall diet quality, several individual food groups showed significant associations with depressive symptoms. Women consuming at least four servings of non‑citrus whole fruits per week exhibited the lowest odds of depression. Whole fruits are rich in micronutrients and polyphenols, which are known to reduce oxidative stress and neuroinflammation mechanisms increasingly implicated in perinatal mood disorders (Fu et al. 2025; Guo and Yang 2024; Jie et al. 2025; Tabaeifard et al. 2025). Contrary to expectation, consuming four or more servings of refined grains per week was also associated with lower odds of depression. In Nepal, refined grains typically refer to polished rice or breads, such as roti, often consumed as part of mixed meals with lentils, vegetables, and fermented pickles. Thus, this association may reflect greater dietary diversity rather than refined grain intake in isolation. Additionally, the availability of refined grains may serve as a cultural marker of food security and abundance, potentially contributing to a more positive outlook and fewer depressive symptoms.

Conversely, several food groups were associated with increased odds of depression risk. First, women consuming beans and pulses ≥ 4 servings per week had more than twice the odds of depression risk compared to those with lower intake. In the context of Nepal, heavy reliance on these inexpensive staples may indicate low dietary diversity, monotony, or economic hardship (Bhandari et al. 2016; Campbell et al. 2014; Sapkota et al. 2017). Furthermore, pulses are high in phytates, which can impair the absorption and bioavailability of micronutrients such as iron, zinc, and B‑vitamins, which are implicated in mood regulation (Kumari and Roy 2023). Higher fish intake (≥ 2 servings/week) was also unexpectedly associated with greater odds of depression. In peri‐urban Nepal, fish is often batter‐fried, negating its nutritional value. Additionally, fish accessible to urban or peri‐urban households is frequently locally sourced from polluted waters, higher in heavy metals like mercury and lead, and increasingly contaminated with microplastics and organophosphate pesticide residues, all of which are known to induce oxidative stress and neuro‑inflammatory cascades that have been mechanistically linked to depressive symptomatology (Acharya et al. 2023; Malla‐Pradhan et al. 2022; Albanese et al. 2012; Sălcudean et al. 2025; Thapa et al. 2014). Third, higher whole grain consumption was associated with increased odds of depression risk, contrary to the literature supporting complex carbohydrates for mental well‑being(Chen et al. 2023). This finding may be explained by confounding factors, such as socioeconomic status or health conditions like gestational diabetes mellitus (GDM) (Jin et al. 2024; Tasnim et al. 2022). Whole grains like millet or buckwheat are often consumed more frequently in lower‐income households due to accessibility and affordability, and are recommended for women with GDM or pre‐existing diabetes, who may already be experiencing heightened stress and poorer mental health during pregnancy. Lastly, processed foods, including processed meat, baked goods, sugar‐sweetened beverages, fried items, and desserts, showed the strongest positive associations with depression, supporting the view that refined sugars, trans‑fats, and excess sodium may promote systemic inflammation and gut dysbiosis (Contreras‐Rodriguez et al. 2023; Lutz et al. 2025; Marano et al. 2023; Mohan et al. 2023). Such patterns may also reflect emotion‐driven or compensatory eating behaviors among individuals experiencing poor mental health. However, our cross‐sectional design limits the ability to disentangle directionality, whether unhealthy eating contributes to depressive symptoms, or depression leads to greater consumption of ultra‐processed foods.

4.1. Strengths and Limitations

This study has several strengths. It utilised a validated dietary assessment tool (PrimeScreen) and a standardised measure of depression (EPDS), along with detailed adjustment for a broad range of sociodemographic, obstetric, and lifestyle covariates. The study was conducted in a well‐defined cohort of pregnant women in a peri‐urban Nepali setting, offering context‐specific insights from an LMIC population often underrepresented in perinatal nutrition and mental health research. However, several limitations must be acknowledged. Although this study was nested within a longitudinal cohort, both dietary intake and depressive symptoms were assessed concurrently in the third trimester. As such, given the cross‐sectional design, causality cannot be established, and it remains unclear whether poor dietary patterns contribute to depressive symptoms or if pre‐existing depression negatively affects dietary choices, a directionality issue echoed in prior research (Dabravolskaj et al. 2024; Myrissa et al. 2024; Quirk et al. 2013; Sparling et al. 2016). Additionally, while PrimeScreen offers a pragmatic and culturally adapted approach, it is a brief screener rather than a comprehensive dietary assessment tool. As such, total caloric intake could not be estimated, and adjustment for energy intake in the models was not possible. Although we adjusted for key indicators of socioeconomic status as well as clinical covariates (including age, education, ethnicity, occupation, parity, and gestational week), unmeasured residual confounding may still influence the observed associations. Another limitation is that PrimeScreen may not fully capture the nuances and complexity of Nepal's diverse and seasonal dietary patterns, which could affect maternal mental health. Primescreen also does not capture micronutrient indicators such as vitamin D status, despite growing evidence linking vitamin D deficiency to depressive symptoms (Accortt et al. 2016; Gould et al. 2022) and given its high prevalence in South Asian populations (Siddiqee et al 2021). To advance understanding of the nutritional determinants of perinatal depression, future studies should incorporate biomarker assessments of key micronutrients, particularly serum vitamin D. Vitamin D deficiency may contribute to the development of perinatal depression through several biological pathways, including dysregulation of serotonin synthesis, reduced neurotrophic support, and heightened systemic inflammation (Cui and Eyles 2022; Patrick and Ames 2014). The study was also conducted at a single hospital site, which may limit the generalizability of findings to other regions or populations. Future research should include longitudinal designs, larger and more diverse samples, and comprehensive, culturally sensitive dietary assessment tools. Such work is especially important in resource‐limited settings, where evidence on the intersection of diet and perinatal mental health remains sparse.

5. Conclusion

This study contributes to the growing body of evidence linking maternal diet quality to perinatal depression, particularly within the context of LMICs like Nepal. We found that higher overall diet quality was significantly associated with a lower likelihood of depression in the third trimester. These findings underscore the potential value of integrating nutritional assessment and education into routine antenatal care as a practical approach to identify and support women at risk for poor diet quality and depressive symptoms, thereby potentially helping to mitigate the intergenerational effects of both conditions. Given that dietary factors are linked not only to mental health but also to key pregnancy complications such as gestational diabetes and excessive gestational weight gain, incorporating routine diet quality assessment may offer a more comprehensive and proactive strategy than depression screening alone. However, further research is needed to understand the complex interplay of cultural, socioeconomic, and psychological factors influencing diet and depression among pregnant populations in LMICS, and to inform the development and implementation of targeted dietary interventions and clinical recommendations at both the community and population levels. Group‐based delivery of nutrition education and community‐based mental health counselling during antenatal visits, led by local health workers familiar with the cultural and socioeconomic context, may enhance accessibility, foster social support, and help reduce stigma surrounding mental health. Nonetheless, much more research is needed to develop, adapt, and rigorously test tailored and culturally appropriate interventions that address the intersecting nutritional and psychosocial needs of pregnant women across diverse low‐resource settings.

Author Contributions

K.C. analysed and interpreted the patient data on the association between diet quality score and risk of depression. S.T., R.K. and S.P. collected the data. S.R. and A.S. conceptualized the study. K.C. and S.R. wrote the manuscript, while A.S., B.S., P.P., P.R., N.S. and B.P. reviewed it. All authors read and approved the final version of the manuscript.

Disclosure

The submitted manuscript is an original work and has not been published anywhere, nor is it under publication elsewhere. The authors declare no relevant financial or nonfinancial interests.

Ethics Statement

The ethical/institutional review board of Kathmandu University School of Medical Health Sciences (KUIRC Approval number: 10/23) approved the study. Trained research assistants thoroughly explained the study's objectives, risks, benefits, participants' rights to voluntary participation, and the ability to withdraw at any time. Participants voluntarily joined the study after providing written informed consent.

Consent

All participants provided informed consent. Patient consent for publication: Not applicable.

Conflicts of Interest

The authors declare no conflicts of interest.

Acknowledgements

Shaun Ranade gratefully acknowledges financial support for this study by the Fulbright U.S. Student Program, which is sponsored by the U.S. Department of State, as well as the Fogarty International Center of the National Institutes of Health under grant #D43TW009345 awarded to the Northern Pacific Global Health Fellows Program. Its contents are solely the author's responsibility and do not necessarily represent the official views of the Fulbright Program, the National Institutes of Health, or the Government of the United States.

Chaudhary, K. , Ranade S., Paudel P., et al. 2025. “Maternal Diet Quality and Third‐Trimester Depression: Insights From a Nepali Birth Cohort Study .” Maternal & Child Nutrition 22: 1–12. 10.1111/mcn.70146.

Joint senior authors: Archana Shrestha and Shristi Rawal.

Data Availability Statement

Data and materials are available upon reasonable request to the author.

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