by This cross-sectional study was conducted in Kabul, a city with more than 160 health facilities and 22 municipalities. [13]. We used a multi-stage random sampling technique, in each zone of Kabul (North, East, West and South), we first randomly selected one municipality and from each municipality we randomly selected one health center. Three comprehensive health centers (CHCs) were selected from the 15th (North), 13th (West), and 9th (East) municipalities. [14]. One district hospital was selected from the 16th municipality (south) [14]. From these health centers, we took a convenience sample of 421 women of reproductive age. Equal samples of women of reproductive age (n = 105) from three of the health centers and (n = 106) from one health center were included according to the following formula [15]:
α = 0.05.
Z = 95% (1.96)
p= 47 (p= 47%, overweight prevalence among affluent women of reproductive age) [16].
q= (1-0.47) = (0.53)
$$n\, = \, \frac{{{{({Z_{1 – \frac{\alpha }{2}}})}^2}pq}}{{{(r)}^2} } }\, = \, \frac{{{{(1.96)}^2}\,0.47\,x\,0.53}}{{{(0.05)}^2}}}\, = \,382.7 \, \about \,383$$
For the non-response coverage of the study, we add the 10% to our sample size, which is calculated as 10% of the participants = (10*383)/100 = 38 people. We then added 38 to our total sample size, 38 + 383 = 421 participants. An equal number of women attending a procedure (n = 105) were sampled from each of the four health centres. This study included women of reproductive age (15-45 years) who agreed to participate in this study and were clients of selected clinics, as well as women who had no dietary restrictions. We excluded women of reproductive age who had previously been diagnosed by a psychiatrist with a mental disorder.
Assessment of dietary intake
All women’s dietary intakes were assessed using a 24-hour recall questionnaire, which is a reliable and validated method. [17, 18]. Dietary intakes were collected on three weekdays, two weekdays and one weekend day [19]. To facilitate data collection, interviewers used a variety of tools to determine serving sizes, including can sizes, handfuls of bread, tablespoons, teaspoons, ladles, plates, bowls , glasses, and photographs of common household meals. Portion sizes were estimated based on household eating/cooking equipment and quantities were reported. The quantities were then entered into Nutritionist 4 (NUT4) software for nutrient adequacy analysis and converted to grams. Total and mean intakes of each food and nutrient consumed were then calculated.
Assessment of anthropometric indices
Anthropometric indices, such as weight, height, waist circumference and body mass index (BMI) were measured and calculated for each participant. BMI was calculated by dividing weight (kg) by height2 (m). A calibrated digital scale (SECA 831, Germany) was used for weight measurements. Waist circumference (WC) was measured using a flexible, unstretched anthropometric tape at a point midway between the lowest rib and the upper edge of the iliac crest, after normal expiration and without applying pressure to the body surface. We used World Health Organization (WHO) BMI classifications for adults: BMI <18.5 for underweight, BMI = 18.5 to 24.9 for normal weight, BMI = 25 to 29.9 for overweight, and BMI ≥ 30 for obese [20].
Assessment of common mental health problems
We used the Depression, Anxiety and Stress Scale − 21 Items (DASS-21), a series of self-report scales to assess symptoms of depression, anxiety and stress. The DASS-21 scale has one section for depression, another for anxiety and another for stress (each section has seven items) [21]. Depressive symptoms were assessed by assessing dysphoria (a general feeling of dissatisfaction with life), hopelessness, devaluation of one’s life, self-esteem, lack of interest, anhedonia (inability to find pleasure), and lethargy (tendency to to do something). The anxiety component includes autonomic arousal, skeletal muscle responses, and subjective feelings of anxiety. The stress component is sensitive to persistent nonspecific stimulus levels. The reliability and validity of this instrument has been measured locally and internationally and is available in 34 languages [22].
CMHPs are classified into three categories: normal, moderate and severe. The cut-off point for depression (normal < 13, measartha = 14 go 21, agus dian> 22), for anxiety ((normal <9, measartha = 10 go 14, agus trom> 15), and for (normal) stress. < 18, measartha = 19 go 25, agus dian > 26).
Assessment of methyl donor nutrient intake
Nutrient intake of methyl donor nutrients (Vitamin B2, B6, folate, B12, methionine, betaine and choline) was calculated using Nutritionist 4 (NUT4) software. The aforementioned nutrients were extracted for each participant and then each of these nutrients was calculated for each individual from the 1st to the 10th decile. Total scores were obtained by summing each participant’s scores (range 7 to 68) and categorizing them into triplicates. We made the cut-off points C1 < 23, 46 < Q2 > 23 and > 46 for Q3 to consider. We then evaluated demographic variables such as women’s age, level of education, monthly family income, marital status, place of residence, history of disease (blood pressure and diabetes) among the territories.
Physical activity questionnaire (IPAQ)
The IPAQ is a self-report questionnaire developed in 1998. The reliability and validity of this questionnaire was tested in 12 countries in 2000. The final results indicate that it is an acceptable measure in many countries and languages. We used the long form of the IPAQ, which has four sections (work, cycling/transport, home/garden and leisure) with 27 items. [23] and three categories of vigorous, moderate and light physical activity in the past 7 days [24].
Statistical analysis
The amount of nutrients consumed by each participant was calculated using Nutritionist IV software. The data was analyzed using the Statistical Package for Social Science software (SPSS Version 26). For general characteristics of individuals, Chi-square tests and one-way ANOVAs were performed. To assess dietary intake of methyl donor nutrients and food groups, we used the residual model test while adjusting for energy intake. We fitted logistic regression models to estimate the risk of CMHPs based on methyl donor tertile..
