Diet Quality as an Associated Factor for Liver Function Enzyme Abnormalities Among Women of Reproductive Age
Fatehatun Noor, Nusrat Jahan Shorovi, Md. Mahadi Hasan Shoruve, Tasmim Fahima Ahmad, FNU Asamoni, Nisarga Bahar, Md. Ruhul Amin, Tanjina Rahman, Abu Ahmed Shamim, M Akhtaruzzaman
TL;DR
Poor diet quality is linked to abnormal liver enzymes in Bangladeshi women of reproductive age, suggesting dietary improvements could help prevent liver issues.
Contribution
The study identifies low Food Consumption Score as a novel risk factor for elevated liver enzymes in this population.
Findings
28.7% of participants had elevated ALT and/or AST levels.
Low Food Consumption Score significantly increased the odds of elevated liver enzymes.
Higher saturated fat and cholesterol intake were associated with abnormal liver enzymes.
Abstract
Background Elevated liver enzymes, such as alanine aminotransferase (ALT) and aspartate aminotransferase (AST), are early indicators of liver dysfunction in non-alcoholic fatty liver disease (NAFLD). The prevalence of NAFLD is increasing in Bangladesh, with studies indicating a higher prevalence among women. Additionally, rapid dietary transitions toward energy-dense foods rich in fat and sugar have contributed to the rising burden of non-communicable diseases. This study aimed to investigate the association between dietary intake, diet quality indicators, and elevated liver enzymes (ALT and AST) among women of reproductive age in Bangladesh. Methods A cross-sectional study was conducted among 240 women of reproductive age (15-49 years) randomly selected from community households in three selected districts of Bangladesh. Anthropometric and socioeconomic data were collected using…
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| Characteristics | Liver enzyme status | Test statistic (χ²) | p-value | |
| Normal n (%) | Elevated n (%) | |||
| 171 (71.3) | 69 (28.8) | |||
| Age (years) | ||||
| ≤23 | 59(34.5) | 16(23.2) | 4.04 | 0.133 |
| 24–28 | 56(32.8) | 31(44.9) | ||
| ≥29 | 56(32.8) | 22(31.9) | ||
| Marital Status | ||||
| Unmarried | 10(5.8) | 4(5.8) | Fisher’s exact | 1.000 |
| Married | 161(94.2) | 65(94.2) | ||
| Place of residence | ||||
| Urban | 38(22.2) | 24(34.8) | 4.05 | 0.044* |
| Rural | 133(77.8) | 45(65.2) | ||
| Food Insecurity | ||||
| Never Experienced | 135(78.9) | 60(87.0) | 2.07 | 0.150 |
| Sometimes Experienced | 36(21.1) | 9(13.0) | ||
| Monthly food expenditure (BDT) | ||||
| ≤6,550.00 | 128(74.9) | 40(58.0) | 8.12 | 0.017* |
| 6,551.00 – 9,640.00 | 25(14.6) | 13(18.8) | ||
| ≥9,641.00 | 18(10.5) | 16(23.2) | ||
| Body mass index (kg/m²) | ||||
| Underweight (<18.5) | 43(25.1) | 10(14.7) | 3.14 | 0.209 |
| Normal (18.5–22.9) | 84(49.1) | 37(54.4) | ||
| Overweight or obese (≥23.0) | 44(25.7) | 21(30.9) | ||
| Food Group | Food Item | Participants consumed by a particular food group n (%) | Amount (g/day) consumed by liver enzyme status | ||||||
| Normal N = 171 | Elevated N = 69 | Test statistic (χ²) | p-value | Normal Median (IQR) | Elevated Median (IQR) | Test statistic (Z) | p-value | ||
| Cereals and tubers | Rice | 171 (100.0) | 69 (100.0) | - | N/A | 457.63 (354.28 - 599.29) | 404.46 (315.43 - 523.64) | 2.602 | 0.009* |
| Wheat | 31 (18.1) | 17 (24.6) | 1.3 | 0.254 | 136.36 (17.31 - 185.93) | 86.79 (39.54 - 173.08) | 0.172 | 0.863 | |
| Potato | 121 (70.8) | 48 (69.6) | 0.03 | 0.854 | 90.91 (54.91 - 148.62) | 109.70 (71.47 - 129.97) | −0.737 | 0.461 | |
| Total | 171 (100.0) | 69 (100.0) | - | N/A | 568.48 (448.34 - 685.96) | 517.47 (395.56 - 650.35) | 1.787 | 0.074 | |
| Vegetables | Non-Leafy Vegetables | 132 (77.2) | 51 (73.9) | 0.29 | 0.589 | 147.25 (87.11 - 229.67) | 163.64 (99.39 - 221.92) | −0.598 | 0.550 |
| Leafy Vegetables | 49 (28.7) | 12 (17.4) | 3.29 | 0.070 | 131.71 (88.34 - 227.85) | 100.00 (59.43 - 123.90) | 1.851 | 0.064 | |
| Other Vegetables | 10 (5.9) | 6 (8.7) | 0.64 | 0.423 | 122.18 (65.45 - 152.54) | 60.14 (19.42 - 107.46) | 1.466 | 0.143 | |
| Total Vegetables | 146 (85.4) | 56 (81.2) | 0.66 | 0.418 | 187.82 (117.39 - 298.18) | 177.00 (102.48 - 227.88) | 0.976 | 0.329 | |
| Pulses | Masoor, Khesari | 55 (32.2) | 30 (43.5) | 2.75 | 0.097 | 32.73 (16.42 - 58.44) | 26.69 (13.43 - 65.45) | 0.740 | 0.459 |
| Milk & Milk Products | Milk and Milk Products | 21 (12.3) | 31 (44.9) | 30.87 | <0.001* | 66.21 (9.82 - 123.29) | 37.55 (3.91 - 110.43) | 1.147 | 0.251 |
| Edible Oils | Soybean Oil | 161 (94.2) | 68 (98.6) | 2.18 | 0.140 | 17.37 (11.74 - 24.32) | 15.78 (8.97 - 27.19) | 1.106 | 0.269 |
| Mustard Oil | 11 (6.4) | 4 (5.8) | 0.03 | 0.854 | 5.14 (2.43 - 15.43) | 3.69 (2.12 - 9.86) | 0.849 | 0.396 | |
| Total | 168 (98.3) | 69 (100.0) | 1.23 | 0.268 | 17.22 (11.74 - 24.24) | 15.43 (9.15 - 27.19) | 0.942 | 0.346 | |
| Meat, Poultry, and Eggs | Flesh food | 27 (15.8) | 15 (21.7) | 1.21 | 0.272 | 141.82 (111.79 - 219.51) | 126.00 (70.76 - 281.25) | 0.368 | 0.713 |
| Eggs | 32 (18.7) | 14 (20.3) | 0.08 | 0.779 | 32.16 (21.06 - 50.37) | 32.90 (26.34 - 44.38) | −0.442 | 0.659 | |
| Fish | 113 (66.1) | 55 (79.7) | 4.35 | 0.037* | 91.31 (51.05 - 152.54) | 73.23 (38.71 - 128.75) | 1.163 | 0.245 | |
| Total | 135 (79.0) | 63 (91.3) | 5.2 | 0.023* | 113.92 (61.02 - 175.85) | 108.00 (50.34 - 175.32) | 0.381 | 0.703 | |
| Condiments and Spices | Onion | 164 (95.9) | 67 (97.1) | 0.19 | 0.659 | 29.31 (19.37 - 42.45) | 25.71 (17.31 - 44.18) | 0.471 | 0.638 |
| Chili | 135 (79.0) | 59 (85.5) | 1.37 | 0.243 | 6.26 (3.93 - 10.85) | 3.53 (2.20 - 7.76) | 3.377 | 0.001* | |
| Fruits | 15 (8.8) | 16 (23.2) | 9.08 | 0.003* | 29.72 (23.48 - 114.55) | 87.33 (17.38 - 190.09) | −0.474 | 0.635 | |
| Sugar | 24 (14.0) | 27 (39.1) | 18.5 | <0.001* | 5.53 (3.45 - 9.39) | 5.33 (4.14 - 10.59) | −0.444 | 0.657 | |
| Salt | 24 (14.0) | 18 (26.1) | 4.95 | 0.026* | 0.10 (0.04 - 0.28) | 0.20 (0.15 - 0.28) | −1.716 | 0.086 | |
| Miscellaneous | 33 (19.3) | 20 (29.0) | 2.68 | 0.102 | 28.48 (14.79 - 39.13) | 33.13 (14.77 - 52.06) | −0.541 | 0.588 | |
| Macronutrients | Normal Median (IQR) | Elevated Median (IQR) | Test statistic (Z) | p-value |
| Energy (kcal) | 2157.51 (1793.24 - 2670.80) | 2171.68 (1600.54 - 2583.28) | 0.92 | 0.360 |
| Carbohydrate (g) | 410.54 (328.91 - 494.39) | 397.48 (300.80 - 464.60) | 1.42 | 0.156 |
| % Carbohydrate | 75 (70 - 78) | 73 (68 - 78) | 1.7 | 0.088 |
| Protein (g) | 62.77 (47.76 - 80.93) | 58.48 (47.76 - 78.95) | 0.4 | 0.687 |
| % Protein | 11 (9 - 13) | 11 (9 - 14) | -0.82 | 0.415 |
| Fats (g) | 25.76 (17.86 - 37.78) | 25.82 (20.97 - 41.72) | -1.14 | 0.256 |
| % Fat | 11 (8 - 15) | 13 (9 - 17) | -2.34 | 0.020* |
| SFA (g) | 4.41 (2.98 - 7.15) | 5.89 (3.84 - 9.63) | -2.56 | 0.010* |
| % SFA | 1.94 (1.33 - 2.79) | 2.50 (1.67 - 4.36) | -3.33 | 0.001* |
| MUFA (g) | 5.39 (3.66 - 8.05) | 6.29 (4.50 - 9.06) | -1.98 | 0.047* |
| % MUFA | 2.36 (1.66 - 3.31) | 3.06 (2.01 - 4.21) | -2.91 | 0.004* |
| PUFA (g) | 12.17 (7.88 - 16.37) | 11.14 (7.85 - 19.58) | 0.24 | 0.808 |
| % PUFA | 5.04 (3.65 - 6.73) | 4.98 (3.56 - 7.68) | -0.4 | 0.687 |
| Cholesterol (mg) | 30.41 (0.00 - 106.61) | 55.21 (15.21 - 137.92) | -2.44 | 0.015* |
| Dietary Fiber (g) | 27.56 (20.24 - 33.98) | 25.64 (18.66 - 32.93) | 1.23 | 0.220 |
| Micronutrient (Unit) | Normal (n=174) Median (IQR) | Elevated (n=70) Median (IQR) | Test statistic (Z) | P-value |
| Vitamin A (RAE μg) | 80.53 (21.98 - 293.31) | 66.30 (23.16 - 281.73) | 0.24 | 0.812 |
| Thiamin (B1, mg) | 4.65 (2.25 - 12.50) | 3.80 (1.91 - 13.88) | 0.71 | 0.481 |
| Riboflavin (B2, mg) | 4.43 (3.21 - 6.60) | 4.49 (3.16 - 6.75) | -0.37 | 0.712 |
| Niacin (B3, mg) | 53.41 (30.31 - 94.11) | 56.46 (27.30 - 83.89) | 0.69 | 0.492 |
| Vitamin B6 (mg) | 1.27 (0.82 - 1.64) | 1.43 (0.88 - 1.73) | -0.47 | 0.638 |
| Folate (B9, μg) | 0.62 (0.46 - 0.88) | 0.65 (0.50 - 0.98) | -0.96 | 0.336 |
| Vitamin B12 (μg) | 18.24 (10.50 - 23.81) | 17.72 (9.58 - 23.63) | 0.34 | 0.738 |
| Vitamin C (mg) | 1.56 (1.09 - 2.03) | 1.60 (0.82 - 2.10) | 0.61 | 0.545 |
| Calcium (mg) | 172.38 (123.37 - 314.91) | 168.07 (116.57 - 471.05) | -0.63 | 0.530 |
| Iron (mg) | 348.55 (276.68 - 442.05) | 333.07 (246.98 - 414.34) | 0.95 | 0.343 |
| Magnesium (mg) | 242.88 (131.64 - 583.36) | 258.06 (149.23 - 504.41) | -0.11 | 0.910 |
| Zinc (mg) | 1072.20 (814.59 - 1318.46) | 992.05 (831.31 - 1315.48) | 0.52 | 0.602 |
| Variables | Categories | Normal n (%) | Elevated n (%) | Test statistic (χ²) | P-value |
| DD | Inadequate (<5 food groups) | 144 (84.2) | 50 (72.5) | 4.38 | 0.036* |
| Adequate (≥5 or more food groups) | 27 (15.8) | 19 (27.5) | |||
| FCS | Poor | 16 (9.4) | 16 (23.2) | 8.52 | 0.014* |
| Borderline | 74 (43.3) | 28 (40.6) | |||
| Acceptable | 81 (47.4) | 25 (36.2) | |||
| BD_HEI | Tertile 1 (Low) | 60 (35.1) | 21 (30.4) | 1.7 | 0.428 |
| Tertile 2 | 59 (34.5) | 21 (30.4) | |||
| Tertile 3 (High) | 52 (30.4) | 27 (39.1) |
| Variables | Crude Model | Model 1 | Model 2 | |||
| COR (95% CI) |
| AOR (95% CI) |
| AOR (95% CI) |
| |
| % Fat | 1.06 (1.01 – 1.11) | 0.018 | 1.06 (1.00 – 1.11) | 0.033 | 1.07 (1.01 – 1.12) | 0.021 |
| SFA (g) | 1.05 (1.01 – 1.10) | 0.028 | 1.05 (1.01 – 1.10) | 0.026 | 1.05 (1.00 – 1.11) | 0.038 |
| % SFA | 1.27 (1.09 – 1.47) | 0.002 | 1.27 (1.09 – 1.47) | 0.003 | 1.28 (1.09 – 1.51) | 0.003 |
| % MUFA | 1.05 (0.99 – 1.11) | 0.083 | 1.05 (0.99 – 1.12) | 0.075 | 1.06 (1.00 – 1.13) | 0.067 |
| MUFA (g) | 1.27 (1.06 – 1.51) | 0.009 | 1.26 (1.05 – 1.50) | 0.012 | 1.30 (1.08 – 1.57) | 0.006 |
| Cholesterol (mg) | 1.00 (1.00 – 1.00) | 0.028 | 1.00 (1.00 – 1.00) | 0.027 | 1.00 (0.99 – 1.00) | 0.085 |
| Variables | Categories | COR | 95% CI |
| AOR | 95% CI |
|
| FCS | Acceptable | Ref | Ref | ||||
| Borderline | 1.23 | 0.66 – 2.29 | 0.523 | 1.39 | 0.72 – 2.66 | 0.460 | |
| Poor | 3.24 | 1.42 – 7.40 | 0.005* | 3.64 | 1.53 – 8.62 | 0.003* | |
| DD | Inadequate | Ref | Ref | ||||
| Adequate | 2.03 | 1.04 – 3.96 | 0.039* | 1.72 | 0.83 – 3.57 | 0.143 | |
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Taxonomy
TopicsLiver Disease Diagnosis and Treatment · Nutritional Studies and Diet · Diabetes, Cardiovascular Risks, and Lipoproteins
Introduction
The liver plays a vital role in digestion, protein synthesis, detoxification, lipid metabolism, and glucose homeostasis [1]. Previous studies in Bangladesh have reported non-alcoholic fatty liver disease (NAFLD) prevalence ranging from 18.5% in a community-based survey to 33.8% among adult volunteers, with both studies consistently showing a higher prevalence among females than males [2,3]. Elevated liver enzymes such as alanine aminotransferase (ALT) and aspartate aminotransferase (AST) are important biomarkers of NAFLD. Previous studies have reported elevated ALT prevalence of 18.8% and AST of 21.6% among general adults [4], with one study involving only female participants found that the elevated ALT and AST were 21.5% and 18.7%, respectively [5].
Dietary patterns and nutrient intake are strongly linked to liver health [6]. High prevalence of vitamin B12, zinc [7], and tocopherol [8] deficiencies has also been reported among pregnant women from Bangladesh. Micronutrient deficiencies, as well as excessive intakes of saturated fats, trans fats, refined sugars, and animal proteins, can contribute to abnormal liver function through mechanisms involving triglyceride accumulation, oxidative stress, and insulin resistance [9]. Similar associations have been observed in Indian and Bangladeshi populations, where dietary intake is a major predictor of liver-related non-communicable diseases [10]. Dietary studies in various populations have shown that low intakes of fruits, vegetables, and milk, combined with higher intakes of red meat, fats, and energy-dense foods, are associated with elevated liver enzymes [11].
Diet quality indices such as Dietary Diversity (MDD-W), Food Consumption Score (FCS), and Bangladesh Healthy Eating Index (BD_HEI) have been used globally to assess nutrient adequacy and diet-disease relationships. DD reflects micronutrient adequacy [12], while FCS evaluates household food access and consumption [13]. However, the relationship between DD and non-communicable diseases is complex and depends on the types of foods consumed. In some settings, increased dietary diversity has been associated with higher fat intake, which may contribute to metabolic disorders [14]. A low FCS may be associated with an increased risk of developing noncommunicable diseases (NCDs), including abnormalities such as elevated liver enzyme levels [15]. HEI assesses adherence to dietary guidelines and has been linked to lower rates of elevated liver enzymes [16] through higher fruit and vegetable consumption [17].
Despite these global insights, there is limited research exploring how dietary intake, FCS, is associated with liver enzyme abnormalities among Bangladeshi women of reproductive age. This study aims to address this gap by examining the relationship between diet quality indicators and elevated liver function markers in this population.
Materials and methods
Study design and population
This cross-sectional study was described in detail previously [5]. In summary, data were collected from the Nutrition, Health, and Demographic Survey of Bangladesh 2011 (NHDSBD-2011), a nationally representative survey designed to assess demographic, health, and dietary patterns across the seven administrative divisions of Bangladesh. For this study, a sub-sample of 240 women of reproductive age, 15 to 49 years, was selected from three districts (Dhaka, Khulna, and Chittagong) [18].
Socio-demographic and anthropometric data
Socio-demographic information, including age, marital status, household expenditure, food security, and lifestyle factors, was collected using a standardized, validated questionnaire. Anthropometric measurements were obtained by trained personnel following standard protocols described earlier [5]. Body mass index (BMI) was estimated using the WHO Asian cut-off [19].
Biochemical assessment
Participants fasted for at least 8 hours before venous blood collection. Samples were drawn using disposable syringes, processed to obtain serum, and transported to Dhaka on dry ice. ALT and AST activities were measured using a semi-automated biochemistry analyzer (DTN-405, DIALAB GmbH, Austria). Quality control was maintained by re-analyzing every 20th sample and repeating 12 random samples to determine the coefficient of variation (CV), calculated as CV = (SD/Mean) × 100. The CVs were 12.82% for ALT and 6.37% for AST [5].
Liver Enzyme Status
The elevated liver enzymes were defined as serum ALT > 34 U/L and AST > 31 U/L for women, consistent with established reference values [20]. Elevated liver enzymes were defined as ALT elevated or AST elevated, or both elevated [5].
Dietary data collection
Dietary data were collected by a single 24-hour recall method at the household level using a validated questionnaire [18]. Individual dietary intake was estimated by applying the Rome scale, which allocates a consumption unit (‘Man value’) to each household member based on age and sex. Males aged ≥14 years were assigned a value of 1.0; females aged ≥11 years and boys aged 11-14 years, 0.90; children aged 7-10 years, 0.75; children aged 4-6 years, 0.40; and children under 4 years, 0.15 [21,22]. Individual intake was then calculated using the following formula:
( \text{Actual intake (g)} = \frac{\text{Family intake (g)} \times \text{Individual Man value}} {\text{Total Man value}} )
Diet quality indicators
Food Consumption Score (FCS)
FCS was calculated for each participant using the information from the seven-day food frequency questionnaire (FFQ) according to the guidelines of the World Food Program (WFP) [23]. The dietary data were categorized into eight food groups: staples, pulses, vegetables, fruits, meat/fish, dairy, sugar, and oil (g). Each food group's consumption frequency was multiplied by standard WFP weights to obtain weighted scores, which were summed to produce the individual FCS. Participants were classified as having poor (0-28), borderline (28-42), or acceptable (>42) diets [24].
Dietary Diversity (DD)
Dietary diversity (DD) reflects the variety of foods consumed by an individual and is commonly used as an indicator of dietary intake [25]. The Minimum Dietary Diversity for Women (MDD-W) serves as a simple, dichotomous population-level indicator for assessing DD at national and subnational levels. DD was calculated using a single-day dietary assessment [26]. Foods were grouped into 10 standard categories (starchy staples, beans and peas, nuts and seeds, dairy products, flesh foods, Eggs, Dark green leafy vegetables, Vitamin A-rich fruits and vegetables, other vegetables, and other fruits). Women of reproductive age (WRA) were categorized as having inadequate DD if they consumed foods from four or fewer food groups of the 10 defined food groups in the previous 24 hours. Conversely, those who consumed at least five of the 10 food groups were considered to have adequate DD [27].
BD-Healthy Eating Index (BD-HEI)
Diet quality was collected from 24-hour recall data, which was categorized into 11 food groups using the Bangladesh Healthy Eating Index (BD-HEI), adapted from the national Food-Based Dietary Guidelines [28]. The components of the BD-HEI were categorized into adequacy, moderation, and optimum groups. Each component was scored from 0 to 5 or 10, with total BD-HEI scores ranging from 0-90. The participants with higher scores indicated better adherence to dietary recommendations. Participants were grouped into tertiles representing low, medium, and high BD-HEI categories [28].
Ethical approval
Ethical approval for this study was granted by the Institutional Review Board (IRB), Faculty of Biological Sciences, University of Dhaka (Permission No. 10/Bio.Sci./2011-2012). Written informed consent from the participants was taken before data collection.
Statistical analysis
All analyses were performed using STATA version 15.1 (Stata Corp LLC, College Station, TX, USA). Descriptive and inferential statistics were performed. Nutrient intakes, including energy and selected vitamins and minerals, were calculated using the Bangladesh Food Composition Tables [29]. Associations between liver enzyme status (normal vs. elevated) and dietary indicators (DD, FCS, BD_HEI), monthly food expenditure and place of residence were assessed using Pearson’s chi-square tests (reported as χ² = value, p). Fisher’s exact test was used where expected counts were <5 (reported as Fisher’s exact, p). Non-parametric comparisons were tested with Mann-Whitney U tests (reported as U = value, p). Logistic regression models were constructed to examine predictors of elevated liver enzymes while adjusting for potential confounders. Variables were included as confounders if prior inferential analyses showed p < 0.10. All models met the multicollinearity assumption, with variance inflation factors (VIF < 2). Statistical significance was defined as p < 0.05.
Results
The study included 240 women aged 15 to 49 years. Among them, 71.3% (n = 171) had normal liver enzyme status, while 28.8% (n = 69) exhibited elevated liver enzyme status (Table 1). Socio-demographic characteristics, including age, marital status, body mass index (BMI), and food insecurity, did not differ significantly between the two groups. However, the place of residence (p = 0.044) and monthly food expenditure (p = 0.017) were significantly associated with liver enzyme status.
*Table 1: Socio-demographic features between study participants with elevated liver enzymes (n = 244)BDT, Bangladeshi Taka; BMI, Body Mass Index: categories based on WHO Asian cut-off [19]; χ², chi-square test; p < 0.05 considered statistically significant.
Table 2 shows differences in the prevalence and quantity of food consumption by liver enzyme status. Higher proportion of participants with elevated liver enzymes consumed milk and milk products (44.9% vs. 12.3%, p < 0.001), fish (79.7% vs. 66.1%, p = 0.037), total animal protein (91.3% vs. 79.0%, p = 0.023), fruits (23.2% vs. 8.8%, p = 0.003), sugar (39.1% vs. 14.0%, p < 0.001), and salt (26.1% vs. 14.0%, p = 0.026) compared to participants with normal liver enzymes. Participants with elevated liver enzymes also consumed a lower amount of rice (404.46 vs. 457.63 g/day, p = 0.009) and chilies (3.53 vs. 6.26 g/day, p = 0.001).
**Table 2: Daily food group consumption in participants with elevated liver enzyme statusIQR, interquartile range; χ², Chi-square test; Z, Mann–Whitney U test; *p < 0.05 was considered statistically significant. Food groups categorized according to the Food Composition
Macronutrient intake by liver enzyme status is shown in Table 3. Participants with elevated liver enzymes had a higher proportion of energy derived from total fat (p = 0.020), along with significantly greater intakes of saturated fatty acids (p = 0.010), monounsaturated fatty acids (p = 0.004), and dietary cholesterol (p = 0.015).
**Table 3: Macronutrient intake and percentage of energy contribution by elevated liver enzymes statusZ, Mann–Whitney U test; IQR, interquartile range; SFA, Saturated Fatty Acid; MUFA, Monounsaturated fatty acid; PUFA, Polyunsaturated fatty acid; *p < 0.05 was considered statistically significant. Nutrient values estimated using the Food Composition
Table 4 shows that intakes of all assessed micronutrients did not differ significantly between participants with normal and elevated liver enzymes. Median intakes of vitamins A, D, E, C, B1, B2, B3, B6, and folate, as well as minerals including magnesium, calcium, phosphorus, sodium, potassium, zinc, and iron, were similar across groups (all p > 0.05).
**Table 4: Micronutrient intake in participants with elevated liver enzyme statusZ, Mann–Whitney U test; *p < 0.05 was considered statistically significant. Nutrient values estimated using the Food Composition
Table 5 shows the association of diet quality indicators with liver enzyme status. Higher proportion of participants with elevated liver enzymes consumed adequately diversified diet (≥5 food groups: 27.5% vs. 15.8%, p = 0.036) and poorer FCS (23.2% vs. 9.4%, p = 0.014), whereas no significant differences were observed in Healthy Eating Index tertiles between groups.
Table 5: Distribution of dietary quality indicators by liver enzyme statusDD, Dietary Diversity [27]; FCS, Food Consumption Score [24]; BD_HEI, Bangladesh Healthy Eating Index [28]; χ², Chi-square test; p < 0.05 was considered statistically significant.
Binary logistic regression demonstrated that dietary fat intake was significantly associated with elevated liver enzymes (Table 6). In the fully adjusted model, a higher proportion of energy from fat intake was associated with increased odds of elevated liver enzymes (AOR: 1.07; 95% CI: 1.01-1.12; p = 0.021). Saturated fat intake (AOR: 1.05; 95% CI: 1.00-1.11; p = 0.038) and monounsaturated fat intake (AOR: 1.30; 95% CI: 1.08-1.57; p = 0.006) were also independently associated with elevated liver enzymes.
*Table 6: Logistic regression analysis of associations between elevated liver enzyme levels and macronutrient intakeCOR, Crude Odds Ratio; AOR, Adjusted Odds Ratio; SFA, Saturated Fatty Acid; MUFA, Monounsaturated Fatty Acid; Model 1 adjusted for monthly food expenditure and place of residence; Model 2 adjusted for Model 1 variables and dietary diversity and Food Consumption Score; p < 0.05 was considered statistically significant.
Table 7 shows that, after adjusting for monthly food expenditure and place of residence, participants with poor FCS had more than threefold higher odds of elevated liver enzymes compared with those with acceptable FCS (AOR: 3.64; 95% CI: 1.53-8.62; p = 0.003). While adequate DD was associated with higher odds in the crude model (COR: 2.03; 95% CI: 1.04-3.96; p = 0.039), this association was no longer significant after adjustment.
*Table 7: Logistic regression analysis of associations between FCS and elevated liver enzymesOR, Odds Ratio; AOR, Adjusted Odds Ratio; FCS, Food Consumption Score [24]; DD, Dietary Diversity [27]; p < 0.05 was considered statistically significant.
Discussion
This study investigated the association between diet quality and elevated liver enzymes among women of reproductive age in Bangladesh. Elevated liver enzymes were significantly associated with higher intake of total fat, SFA, and MUFA. Poor diet quality, indicated by low FCS, was strongly linked to elevated liver enzymes after adjusting for food expenditure, place of residence, and DD.
The present study found that higher fat intake, particularly SFA and cholesterol, was associated with a higher prevalence of elevated liver enzymes. The type and amount of fats can contribute to liver dysfunction. Saturated fatty acids (SFA) have adverse effects on lipid and glucose homeostasis, which can further aggravate liver dysfunction [30]. In Cameroonian adults, excessive fat consumption results in excess body fat accumulation, and increased visceral fat enhances the flow of free fatty acids into the liver, contributing to hepatic steatosis [31]. Among Indian adults, those with elevated liver enzymes tended to consume diets high in total fat and saturated fat but low in PUFAs [32].
In our study, participants with elevated liver enzymes consumed a higher intake of fat, especially cholesterol, reflecting poor FCS compared to those with normal liver enzymes. Similar findings were observed among Indian adults, where the intake of fat was higher among those with elevated liver enzymes as compared to those with normal liver enzymes [32]. A low FCS may be associated with an increased risk of developing non-communicable diseases (NCDs) [15].
The present study found that elevated liver enzymes were not associated with adequate DD and BD_HEI, while poor FCS was highly associated with the prevalence of elevated liver enzymes. Diet quality indices derived from single-day 24-h recall dietary intake, such as DD and BD_HEI, did not capture the actual habitual consumption of the participants; rather, 7-day FFQ-based FCS captures the day-to-day variation and usual diet [33]. To further explore the diet and liver disease association, both the 24-h recall and FFQ-based dietary assessment tools are recommended.
Overall, these findings highlight that both macronutrient composition and overall diet quality are key determinants of liver health. Interventions that promote balanced, nutrient-dense diets with adequate fruit and vegetable intake and moderate fat consumption may contribute to preventing elevated liver enzyme levels among Bangladeshi women.
Strengths and limitations
This study highlights the association between diet quality and elevated liver enzymes, using validated dietary assessment tools (24-hour recall and FFQ) and established FCS cutoffs [23]. The study has certain limitations, notably its cross-sectional design, which limits the ability to draw causal inferences. Grouping ALT-only, AST-only, and combined elevations into a single binary outcome may have masked etiologic differences among distinct liver enzyme abnormality patterns. Individual dietary intake was estimated from household-level data using the Rome scale, which, although widely applied, may result in an underestimation of intake for women due to unequal intra-household food distribution. Important confounding variables such as physical activity, alcohol intake, and viral hepatitis status could not be included due to a lack of data. In addition, the study sample was not nationally representative, which may limit the generalizability of the findings. Future research should include broader populations and longitudinal approaches.
Conclusions
In summary, the study reveals that low FCS is associated with elevated liver enzymes among reproductive-age women in Bangladesh. High fat intake appears to adversely influence FCS, which may also be related to elevated liver enzymes among reproductive-age women in Bangladesh. Implementing targeted nutritional interventions that encourage balanced and diverse diets is essential to prevent elevated liver enzymes and related health issues.
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