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Minimum diet diversity and its association with nutrient intake among non-pregnant and non-lactating women residing in Delhi
For correspondence: Prof Bani Tamber Aeri, Department of Food and Nutrition, Institute of Home Economics, University of Delhi, New Delhi 110 016, India e-mail: bani.aeri@ihe.du.ac.in
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Received: ,
Accepted: ,
How to cite this article: Sachdeva B, Puri S, Aeri BT. Minimum diet diversity and its association with nutrient intake among non-pregnant and non-lactating women residing in Delhi. Indian J Med Res. 2026;163:802-9. doi: 10.25259/IJMR_2722_2025.
Abstract
Background and objectives
Indian women are disproportionately affected by nutritional deficiencies and are vulnerable to malnutrition. Minimum dietary diversity, an indicator of dietary diversity, has been used widely for pregnant and lactating mothers or rural Indian women; however, there is a deficit of data on dietary diversification among non-pregnant, non-lactating women living in urban areas. This study aimed to assess the Minimum dietary diversity and its association with nutrient intake among non-pregnant, non-lactating NPNL women of Delhi.
Methods
A cross-sectional study was conducted among 400 women (25-49 years) in Delhi. Two-day 24 hr dietary recall (weekend and weekday) and diet quality questionnaire were used to collect the nutrient intake and diet diversity data, respectively. Minimum dietary diversity by Food and Agriculture Organisation was utilised to calculate the diet diversity score. Binary logistic regression and multivariate linear regression analysis were conducted to find predictors of adequate diet diversity and an association between nutrient intake and dietary diversity score, respectively.
Results:
The mean dietary diversity score was 4.1±1.21, and the prevalence of adequate diet diversity was noted to be 38%. Higher age [Odds ratio (OR):1.05; 95% confidence interval (CI):1.01-1.09; P=0.018] high household income (OR:1.00; 95%CI:1.00-1.00; P=0.008) and higher education (OR:59.06; 95%CI: 7.61-457.90; P<0.001) were identified as the predictors of better minimum dietary diversity. A higher minimum diet diversity was significantly associated with protein, mono-unsaturated fatty acids and retinol intake.
Interpretation and conclusions
There is poor dietary diversity and micronutrient adequacy among women in Delhi.
Keywords
Diet quality questionnaire
Macronutrients
MDD-W
Micronutrients
non-pregnant and non-lactating women
The concurrent burden of malnutrition, encompassing overnutrition (obesity, non-communicable diseases) alongside micronutrient deficiencies, is rising as a silent epidemic in India. Almost 56% of the total disease burden in India is attributed to poor dietary choices.1 Indian dietary pattern exhibits a significant deviation from the Eat-Lancet dietary framework as well as from the recommended Indian dietary guidelines by the Indian Council of Medical Research (ICMR).2 Literature suggests that Indian women are disproportionately affected by nutritional deficiencies and, therefore, are more vulnerable to malnutrition. Based on a 15-year analysis of data from the National Family Health Surveys (NFHS-1 to NFHS-5), the adverse health consequences of overweight/obesity, along with anaemia, have increased by almost 130% among women aged 15-49 years.3
Data highlights that compared to men, the prevalence of non-consumption of certain food groups was higher among women.4 Specifically, the odds of not having whole grains (OR-2.69) and vegetables (OR-1.31) in the Indian women’s diet were statistically greater as compared to men. An inadequate diet, rich in fats and sugars and low in fruits and vegetables, has been recognised as a significant contributor to the high rates of micronutrient deficiencies in India.5
A diversified diet with an adequate intake of all food groups ensures dietary diversity and can be adopted as a nutritional approach to address the coexistence of multiple forms of malnutrition. A longitudinal study conducted among the Chinese population revealed that with increase in dietary diversity, the risk of all-cause mortality can be reduced significantly (22%).6 In India, a community-based case-control study conducted among women of reproductive age found that likelihood of low-birth weight can be reduced by 21% with increasing maternal dietary diversity (OR:0.79).7
According to the Food and Agriculture Organization (FAO)8 minimum diet diversity score (MDD-W) takes into account intake of at least five out of ten defined food groups [including grains, white roots and tubers, and plantains; pulses (beans, peas and lentils); nuts and seeds; dairy products; meat, poultry, fish; eggs; dark-green leafy vegetables (DGLV); other vitamin A-rich fruits and vegetables; other vegetables; and other fruits]. There are various studies available which suggest that a relationship exists between dietary diversity and nutrient intake.9-11 However, this association remains underexplored among non-pregnant and non-lactating women, particularly among urban women.
Women are the primary decision makers in Indian households. However, challenge of nutritional deficiencies among women has persisted for many years. As evidenced in the literature, maximum studies and surveys that assessed dietary diversity in India included pregnant and lactating mothers7,12-14 or had been conducted in the rural India.15 Considering that there is a deficit of data on diet diversification among urban non-pregnant non-lactating women, this study was conducted with an aim to assess the minimum dietary diversity and its association with nutrient intake among these women from Delhi.
Methods
This cross-sectional study was undertaken by the department of Food and Nutrition, Institute of Home Economics, University of Delhi, Delhi, India. Ethical clearance was obtained from the Institutional Ethics Committee of the Institute of Home Economics, University of Delhi. Informed written consent was obtained from all women. For illiterate participants, information was read aloud, and written consent was obtained through a thumb impression on the consent form.
Study design and sampling
The present study is a part of a larger cross-sectional study, wherein the objective was the assessment of the food environment, diet, and prevalence of overweight/obesity levels of the women. Three districts of Delhi were chosen (South Delhi, South-West Delhi, and South-East Delhi) based on proximity to researcher’s institution and place of residence. To ensure a consistent food environment for the included women, central points were chosen in each district, and a 1 km circular buffer was created around central points in each district. Data were collected via door-to-door visits to consenting women within these buffer zones. An equal number of participants were recruited (133 from two districts and 134 from one district) from each district using purposive sampling technique.
Study population and estimation of sample size
This study is a part of a large study, wherein assessment of prevalence of overweight/obesity was the primary objective. The minimum sample size of 348 was determined based on a prevalence (34.5%) of overweight adults in Delhi,16 absolute precision of 5% and 95% confidence levels. Considering a 10% expected non-response, data were collected from 400 participants. In addition, a post-hoc assessment was also done to confirm that the achieved sample was adequate for detecting the key associations observed.
Women aged 25–50 years, who were permanent residents in Delhi, were recruited. Women above 50 years were excluded to avoid confounding influence of menopause-related changes in diet and health. Pregnant, lactating, and women with chronic illness or physically challenged were excluded. To avoid bias, only one woman was enrolled from each household.
Data collection
Data were collected at community level between February-December 2023. Face-to-face interviews were conducted, and a questionnaire-cum-interview schedule was utilised to record the information about participants’ sociodemographic and family profiles. A two-day 24-h dietary recall method and diet quality questionnaire (DQQ) was used to collect the data related to nutrient intake and dietary diversity, respectively.
Assessment of dietary diversity score
Dietary diversity was assessed using DQQ. DQQ is a standardised ready-to-use tool that gathers information about the consumption of various food groups. It encompasses a short list of sentinel foods that are consumed frequently. DQQ consists of 29 questions, all closed ended (Yes/No). Till now, this questionnaire has been adapted for use in more than 120 countries and was validated and utilised to gather data in more than 8518 countries, including India18 during the Gallup World Poll Survey (2021-2023).
As per FAO, dietary diversity score can be assessed by determining women’s prior-day consumption of foods from 10 food groups. These food groups include starchy food including grains, white roots and tubers, and plantains; pulses (beans, peas, and lentils); nuts and seeds; dairy products; meat, poultry, fish; eggs; dark green leafy vegetables; other vitamin A-rich fruits and vegetables; other vegetables; and other fruits.
Women were asked to recall food they consumed on the previous day. Then, list of the food items given in DQQ that corresponded to aforementioned food groups was read out to participants exactly as stated in the questionnaire. Responses were recorded as a yes/no. A response of ‘yes’ was assigned ‘1’, while ‘no’ was scored ‘0’. The scores were aggregated to calculate dietary diversity score. Minimum dietary diversity score ranges from 0-10 based on number of food groups consumed previous day. A score of ≥5 signifies that a woman consumes five or more food groups, which is classified as adequate dietary diversity. Conversely, a woman with a score <5 is considered to have inadequate dietary diversity.8
Assessment of nutrient intake
For dietary assessment, DietCal software (version-13), Profound Tech Solution, was utilised. This software calculates nutrients based on values given in the Indian food composition tables (IFCT), 2017.19 Dietary data were collected for two days- one working day and one holiday (preferably consecutive days) using 24-h dietary recall method and nutrient intake of the participants was assessed quantitatively. Subjects were asked to report number of meals they consumed in last 24-h, which included dishes (in terms of household measures) and raw food items consumed within a meal. To determine portion size of cooked food, photographs of standardised recipes of different dishes (curries, parathas) as given in terms of household measures (Katori, spoons, plate, etc.) were taken from the DietCal database and were shown to the participants. Weights of raw food items (example- fruits) were taken from the textbook of Art and Science of Cooking (2020). Questions such as ‘cooking procedure’, ‘additions after cooking or while serving’, ‘food items consumed outside home’ were also inquired. Recipes and nutritional information of a few dishes which participants consumed from fast-food chains were gathered from official websites of commercial food establishments. DietCal estimated the mean nutrient intake of two days, which were compared with the estimated average requirements (EAR) for Indians.
Statistical analysis
Data were cleaned, and statistical analysis was done using SPSS (version 30) software. Univariate analysis was applied to categorical data to obtain means, frequencies, and percentages. Chi-square test was applied to determine the association between socio-demographic profile and dietary diversity score. Kruskal-Wallis’s test was used to compare median nutrient intake between groups with inadequate vs. adequate dietary diversity score. To assess impact of various sociodemographic predictors on adequate dietary diversity, a binary logistic regression model was performed. To associate nutrient intake with dietary diversity score, a linear regression was performed (adjusting for socio-demographic and dietary habits). The level of significance was taken as P value (<0.05). The odds ratio (logistic) and β-coefficient (linear) were estimated at a confidence interval of 95%.
Results
Sociodemographic profile of participants (N=400) is shown in Table I. All had a sedentary lifestyle (sitting ≥ 8 h/day)20 while an almost equal number of participants were vegetarians (n=183, 45.7%) and non-vegetarians (n=182, 45.5%); 35 (8.7%) were eggetarians.
| Variables | Frequency (N=400) | Inadequate dietary diversity (consuming<5 groups) (N=400) | Adequate dietary diversity (consuming ≥5 groups) (N=400) | P value |
|---|---|---|---|---|
| Age (yr) | ||||
| 25-34 | 158 (39.5) | 107 (43.1) | 51 (33.1) | 0.105 |
| 35-44 | 166 (41.5) | 100 (40.3) | 66 (43.7) | |
| 45 and above | 76 (19.0) | 41 (16.5) | 35 (23.0) | |
| Education | ||||
| Illiterate | 47 (11.75) | 46 (18.5) | 1 (0.6) | |
| Secondary | 118 (29.50) | 94 (37.9) | 24 (15.7) | <0.001 |
| Senior Secondary | 46 (11.50) | 34 (13.7) | 12 (7.8) | |
| UG and above | 189 (24.0) | 74 (29.8) | 115 (75.6) | |
| Occupation | ||||
| Self-employed | 57 (14.25) | 41 (16.5) | 16 (10.5) | |
| Private or Government Employee | 60 (15.00) | 25 (10.0) | 35 (23.0) | 0.001 |
| Homemaker | 283 (70.75) | 182 (73.3) | 101 (66.4) | |
| Marital Status | ||||
| Married | 360 (90.0) | 229 (92.3) | 131 (86.1) | 0.04 |
| Single/Separated/Widowed | 40 (10.0) | 19 (7.6) | 21 (13.8) | |
| Income | ||||
| Up to 3 lakhs | 154 (38.5) | 131 (52.8) | 23 (15.3) | |
| 300001 to 6 lakhs | 46 (11.5) | 30 (12.1) | 16 (10.5) | <0.001 |
| 600001 to 12 lakhs | 107 (26.75) | 54 (21.7) | 53 (34.8) | |
| 120001 to 18 lakhs and above | 93 (23.25) | 33 (13.3) | 60 (39.4) | |
| Type of family | ||||
| Nuclear | 317 (79.25) | 201 (81.0) | 116 (76.3) | 0.257 |
| Joint | 83 (20.75) | 47 (18.9) | 36 (23.6) | |
| Number of family members | ||||
| ≤4 members | 197 (49.25) | 112 (45.1) | 85 (55.9) | |
| 5-8 members | 185 (46.25) | 125 (50.4) | 60 (39.4) | 0.098 |
| More than 8 | 18 (4.50) | 11 (4.4) | 7 (4.6) | |
Dietary diversity among women
The mean dietary diversity score from 10 food groups was 4.1±1.21. The proportion of women who had adequate diet diversity was only 38% (n= 400). Consumption of various food groups is depicted in the Figure. Table I compares the sociodemographic profile between women consuming adequate (≥ 5) and inadequate (< 5) food groups.
Significant predictors of adequate dietary diversity included age, household income, and higher education. Compared to illiterate women, women with secondary, senior secondary and graduate and higher education levels had a significantly higher odds of adequate dietary diversity ( Table II).
| Variables | Odds ratio (95% CI) | P value | |
|---|---|---|---|
| Age | 1.05 (1.01-1.09) | 0.018 | |
| Income | 1.00 (1.00-1.00) | 0.008 | |
| Educational qualifications | Illiterate | Reference | |
| Secondary | 11.28 (1.46-87.37) | 0.020 | |
| Senior secondary | 14.28 (1.71-119.27) | 0.014 | |
| Graduate and above | 59.06 (7.61-457.90) | <0.001 | |
| Dietary habit | Eggetarian | Reference | |
| Vegetarian | 1.32 (0.57-3.05) | 0.512 | |
| Non-vegetarian | 1.32 (0.79-2.19) | 0.289 | |
| Family | Joint | Reference | |
| Nuclear | 1.25 (0.69-2.26) | 0.462 | |
| Occupation | Homemaker | Reference | |
| Self-employed | 1.18 (0.54-2.56) | 0.675 | |
| Government/Private employee | 0.80 (0.40-1.61) | 0.538 | |
Nutrient intake among women
Nutrient intake was estimated in terms of grams(g), milligrams(mg), and micrograms(µg). Percentage of EAR and recommended dietary allowances (RDA) met by participants is shown in Supplementary Tables I and II, respectively. The median energy intake of women was 1620 kcal. Indian Councill of Medical Research, National Institute of Nutrition (ICMR-NIN, 2020),21 recommendations for energy percent from macronutrients are given in Supplementary Table I. In the present study, median % energy from protein, carbohydrate and fat was 12.11%, 53.8%, 31.3%, respectively, indicating that percentage of energy derived from protein and carbohydrate was within the recommended range, while it exceeded that from fat sources. Among all micronutrients, median intake of folate, vitamin-C and sodium met the recommendations. Consumption of minerals such as calcium, iron, magnesium, and zinc was far below the EAR.
Dietary diversity and associated nutrients among women
Comparative analysis of nutrients among participants with inadequate and adequate dietary diversity highlighted that of the 20 analysed nutrients, differences existed for 11 nutrients ( Table III). Among macronutrients, the median intake of protein, total fat, saturated fatty acids, mono-unsaturated fatty acids and dietary fibre was statistically higher among the group with adequate dietary diversity Vitamin and micronutrient intake between the two groups is also shown in Table III.
| Nutrients | Inadequate (N=400) |
Adequate (N=400) |
Estimated Difference (95% CI) | P value |
|---|---|---|---|---|
| Energy (kcal) | 1582.86 (1378.1,1878.5) | 1670 (1418.5,1934.0) | 55.6 (-23.4 to 137.9) | 0.1 |
| Protein (g) | 47.2 (39.0, 56.0) | 52.6 (44.8, 59.9) | 4.8 (2.2 to 7.3) | <0.001 |
| Total fat (g) | 53.8 (42.7, 66.2) | 61.1 (49.2, 72.4) | 6.3(2.6to 10.1) | <0.001 |
| Carbohydrate (g) | 220.5 (188.1, 259.0) | 215.5 (187.7, 260.8) | -3.2 (-14.6 to 7.7) | 0.6 |
| Mono-unsaturated fatty acids (MUFA; g) | 9.034 (6.3, 12.5) | 10.6 (7.9, 14.5) | 1747.3 (771.1 to 2729.0) | <0.001 |
| Saturated fatty acids g) | 20.6 (14.7, 28.1) | 25.8 (17.3, 32.2) | 3971.4 (1881.1 to 6042.0) | <0.001 |
| Poly-unsaturated fatty acids (g) | 12.1(82.4, 15.8) | 12.7 (9.6, 16.0) | 541.3 (-480.33 to 1574.63) | 0.2 |
| Total dietary fibre (g) | 28.3 (23.0, 33.7) | 30.4 (25.8, 35.2) | 2.0 (0.3 to 3.7) | 0.0 <0.001 |
| Thiamine (mg) | 0.8 (0.6, 1.0) | 0.8 (0.71,1.0) | 0.0 (-0.0 to 0.0) | 0.3 |
| Riboflavin (mg) | 0.4 (0.3, 0.5) | 0.5 (0.4, 0.7) | 0.1 (0.0 to 0.1) | <0.001 |
| Niacin (mg) | 6.6 (5.4, 8.2) | 7.2 (5.7, 8.3) | 0.2(-0.2 to 0.7) | 0.3 |
| Total B6 (mg) | 0.9 (0.7, 1.1) | 1.0 (0.7, 1.2) | 0.1 (0.0 to 0.1) | 0.001 <0.001 |
| Folate (µg) | 205.7 (157.6, 256.8) | 249.2 (201.3, 308.4) | 47.0 (30.7 to 63.6) | <0.001 |
| Retinol (µg) | 306.8 (213.1, 456.3) | 473.7 (333.0, 647.2) | 29.9 (11.5 to 48.9) | <0.001 |
| Ascorbic acid (mg) | 74.7 (52.5, 103.4) | 94.1 (63.4, 134.3) | 19.3 (10.1 to 28.7) | <0.001 |
| Calcium (mg) | 635.2 (454.2, 859.8) | 871.3 (634.1, 1095. 1) | 209.0 (145.9 to 271.6) | <0.001 |
| Iron (mg) | 10.6 (8.8, 12.8) | 11.1 (9., 13.94) | 0.6 (-0.0 to 1.3) | <0.001 |
| Magnesium (mg) | 293.1 (220.2, 355.5) | 302.4 (254.9, 370.8) | 16.5 (-2.9 to 36.0) | 0.09 <0.001 |
| Potassium (mg) | 2032.9 (1670.3, 2530.8) | 2287.1 (1783.8, 2774.2) | 219.8 (71.8 to 361.8) | 0.1 |
| Zinc (mg) | 6.0 (4.6, 7.2) | 6.25 (4.9, 7.0) | 0.0 (-0.2 to 0.4) | 0.6 |
All groups compared by Wilcoxon rank-sum test
Table IV depicts linear multivariate regression, associating dietary diversity with nutrient intake (adjusted for age, education, income, occupation, household size, vegetarian status). Protein and MUFA intake showed a positive association with dietary diversity, while carbohydrate intake was inversely associated. Retinol was positively associated with dietary diversity score. Among the covariates, income age, and education exhibited a significant positive association with dietary diversity.
| Variables | Β-Coefficient (95% CI) | P value | |
|---|---|---|---|
| Age | 0.019 (0.005,0.033) | 0.007 | |
| Income | 0.000 (0.000,0.000) | 0.004 | |
| Protein | 0.023 (0.012,0.034) | <0.001 | |
| Carbohydrate | -0.005 (-0.007, -0.002) | <0.001 | |
| Vitamin C | 0.001(-0.001,0.003) | 0.250 | |
| Retinol | 0.001 (0.001,0.001) | <0.001 | |
| Mono-Unsaturated fatty acids | 0.023 (0.004,0.042) | 0.016 | |
| Type of family | Joint | Reference | |
| Nuclear | 0.037 (-0.200,0.273) | 0.761 | |
| Educational qualifications | Illiterate | Reference | |
| Secondary | 0.502 (0.177,0.827) | 0.003 | |
| Senior secondary | 0.580 (0.186,0.975) | 0.004 | |
| Graduate and above | 1.003 ( 0.659,1.346) | <0.001 | |
| Dietary habit | Eggetarian | Reference | |
| Vegetarian | -0.215 (0.559,0.128) | 0.218 | |
| Non-vegetarian | -0.147 (-0.493, 0.199) | 0.404 | |
| Occupation | Homemaker | Reference | |
| Self-employed | 0.037 (-0.238,0.312) | 0.792 | |
| Government/Private Employee | 0.137 (-0.161,0.434) | 0.367 | |
CI, confidence interval
Discussion
The present study investigated dietary diversity among non-pregnant, non-lactating women of urban areas of Delhi and its association with nutrient intake. The mean dietary diversity score was 4.12±1.21. The proportion of women who had adequate diet diversity was 38%. Our findings are similar to the mean national prevalence rate. As per the global diet quality project (2021)22 only 41% women across the nation met an adequate dietary diversity Score. Considering food groups, grains were the only food group that was consumed by all women, possibly due to the fact that Indians follow a cereal-based diet, where energy contribution from grains is considerably higher.2 The least consumed groups were dark green leafy vegetables (DGLV) (5.5%), eggs (3.25%) and meat and poultry (2%). The poor consumption of DGLVs might be due to their seasonal availability. Occasional consumption of non-vegetarian food might ( Figure) explain the lowest consumption frequency of egg and meat.
In agreement with prior studies23,24 conducted in India, participants in the current study with higher education, and who belonged to a high-income group had significantly higher dietary diversity score. Although significant statistically, the wide confidence interval as observed in case of education levels may indicate a limited precision, possibly due to small subgroup size.
Nutrient intake among women highlighted that percent calories from protein and carbohydrate met the recommendations, while calories from fat were slightly higher. While controlling for potential confounding variables in multivariate linear regression, adequate dietary diversity score was statistically associated with better intake of protein and MUFA intake. A study conducted among Ethiopian women of reproductive age found a significant relationship between intake of plant protein (not complete protein) and diet diversity.25 In the current study, intake of food groups such as pulses, dairy, nuts and oil seeds might be the contributing factors for the higher protein intake among women who met the criteria of diet diversity.
A meta-analysis identified vitamin-D, iron and vitamin-B12 deficiency as three major micronutrient deficiencies in India, prevalent in more than half of the population.26 In the present study also, intake of most micronutrients was suboptimal among women.
Within micronutrients, based on bivariate analysis, association of adequate dietary diversity score was significantly noted with vitamins, particularly with riboflavin, B6, folate, retinol and vitamin-C. A study conducted among Latin American women revealed comparable findings.11 Similar to our results, they reported that intake of vitamins such as retinol, vitamin B6, vitamin-C, and folate was significantly higher among diet diverse group. However, it is important to mention that after accounting for confounding variables, association of adequate dietary diversity score was noted with retinol only, possibly due to cultural and dietary differences.
The present research exhibits both strengths and limitations. Use of a standardised FAO-developed tool for assessment of dietary diversification and two-day dietary recall method for calculating nutrient intake were the major strengths of this study. Along with this, a strong statistical approach and a significant sample size constituted another strength. Limitations included adoption of purposive sampling, lack of consideration for seasonality in data collection and equal sampling across districts, which have ensured comparability; however, might not reflect population proportions. All these can restrict the generalisability of our findings.
To conclude, the present study highlights that there is a lack of diet diversity and micronutrient adequacy among women in Delhi. A higher dietary diversity is significantly associated with protein, MUFA and retinol intake among urban women. Multiple approaches such as supplementation of fruits, vegetables, and dairy products, scaling up the fortification, and agricultural diversification, may serve as the potential policy-level initiatives. At individual level, behaviour change strategies, including promotion of kitchen gardens, small-scale livestock farming and dissemination of nutritional knowledge regarding consumption of seasonal foods and a variety of food groups in the daily diets, would help in enhancing the nutrient adequacy among women.
Acknowledgment
Authors Mr. Vaibhav Miglani for his contribution as Statistician. Authors also acknowledge all the participants for their cooperation.
Author contributions
BS: Conceptualisation, data collection, data compilation, manuscript writing; SP, BTA: Design of the research, manuscript writing. All authors have read and approve the final printed version of the manuscript.
Financial support and sponsorship
The study received funding support from UGC Research Fellowship (190510579688) awarded to the first author (BS) to carry out as the part of the PhD research work.
Conflicts of Interest
None.
Use of Artificial Intelligence (AI)-Assisted Technology for manuscript preparation
The authors confirm that there was no use of AI-assisted technology for assisting in the writing of the manuscript and no images were manipulated using AI.
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