Development and Validation of a Novel Food-frequency Questionnaire for Hemodialysis Patients in Lucknow, India

Introduction

The assessment of dietary intake is an important aspect in the management of patients with chronic kidney disease (CKD) on hemodialysis. While several methods exist, such as dietary recall, weighed food records, and food diaries, food frequency questionnaire (FFQ) is preferred for long-term diet assessment in epidemiologic studies.[12] The use of FFQs in the hemodialysis population has recently been validated in a few studies from France and Indonesia.[34] The target population being studied and nutrients of interest govern the design and development of an FFQ. In India, there are several regional variations in food intake, which makes it imperative that separate FFQs will need to be designed region-wise.[5] In the case of patients with CKD, dietary intakes are modified by restrictions on salt, fluid, protein, and potassium intake, and further limited by the social and economic burden of the disease. Different FFQs have been developed for use in the general population to study cardiovascular and metabolic outcomes in Kerala, Gujarat, Uttar Pradesh, and South India.[678] However, to our knowledge, no dialysis-specific FFQ has been developed so far in India. We intended to develop a FFQ for hemodialysis patients in Northern India, with a primary purpose of assessing the intake of the nutrients of interest to the nephrologist, such as protein, calcium, phosphorus, and potassium. Notably, no previous study has examined the dietary intake of phosphorus and potassium in Indian patients, and the data on protein intake in Indian hemodialysis patients are sparse, at best. A validated FFQ in this setting would serve an important role in further studies assessing protein-energy wasting and mineral-bone disease from this region.

Materials and Methods

Results

The food items identified from the previous year's dietary recall data from the local hemodialysis population were ranked from the highest to lowest, in terms of contribution to total energy intake and the population intake of the nutrients of interest (protein, calcium, phosphorus, and potassium). The items contributing to 90% of the total intake were selected for inclusion in the FFQ. The representative list for energy intake is shown in Table 1. Similar lists were drawn for protein, calcium, phosphorus, and potassium intake and seven more items were added to the list derived from energy intake alone [Table 2].

The clinical, anthropometric, and laboratory parameters of the 84 study participants in the validation study are presented in Table 3. The mean age was 40 years, 62% were male, with around 2/3^rd of the participants from Lucknow and neighboring towns (urban) and 1/3^rd from a rural location. Out of the total, 15% of patients were Muslim, comparing to the 26% of Lucknow's population, according to the 2011 Census.[12] The majority of the patients were from low- and middle-income groups, with an average dialysis vintage of 4 years, within a range between 13 months and 65 months. Around 14% of the patients were diabetic. The mean BMI was 20.2 kg/m².

Energy and nutrient intakes by the FFQ and the 2-day dietary recall method correlated on carrying out the Pearson correlation as well as paired t-tests, as shown in Table 4. The median correlation coefficient was 0.63, with the lowest r value being 0.55 for calcium intake and the highest being 0.73 for energy intake. The correlations remained significant while carrying out the tests with the energy-adjusted nutrient intakes. The correlations improved on using the de-attenuation formula. For energy-adjusted intakes, de-attenuated correlation coefficients ranged from 0.35 to 0.62, and the median r value was 0.50.

When the differences in nutrient intakes between the FFQ and 2-day dietary recall (for energy, protein, calcium, phosphorus, and potassium) were plotted against the mean nutrient intakes of both the methods (Bland-Altman plots), the points were consistently biased toward a positive difference for all the nutrients [Figure 1]. The width of the limits of the agreement for energy, protein, and potassium intake by both methods was considered good as the difference between intakes was approximately equal to 1 standard deviation (SD) of the mean of the intakes. The width of the limits of the agreement for calcium intake was considered fair (approximately 2 × SD) and was poor for phosphorus intake (approximately 3 × SD).[13] Kappa statistics showed slight agreement for calcium, phosphorus, potassium, and protein intakes by both methods and fair agreement for energy intake (κ_w 0.26) as shown in Table 5.

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Figure 1

Bland-Altman plots assessing the validity of the food-frequency questionnaire (FFQ) against the mean of the 2-day dietary recall method

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Discussion

This study demonstrated the design and development of an FFQ, specific to a hemodialysis population in northern India. Indian diet is heterogeneous and shows vast regional and socioeconomic variations, which precludes the use of other FFQs developed in this region, especially so in the CKD population. Hitherto, an FFQ developed for the study of diabetes and metabolic syndrome in high-income group participants from Lucknow was examined for similarities to our 154-item food list, however, it was not seen to be useful in predicting the variance of the nutrients of interest in our dialysis population.[8] This was probably due to the inherent differences in the CKD diet and an admixture of rural and urban participants from predominantly lower and middle-income groups. Therefore, the development of this dialysis-specific FFQ resulted from a need for reliable, longitudinal dietary assessment of this nutritionally at-risk population. This interviewer-administered FFQ developed in our study could be completed in approximately 20–30 min and the interviewer (who was a trained dietician) did not report difficulties in comprehension of the individual food items and portion size estimation (with the provided visual guides for measurement).

One significant problem in computing nutrient intakes is the absence of standardized food composition tables for cooked foods in India, which leads to variations in nutrient estimation when using slightly different ingredients and recipes and also does not account for the loss of nutrients during cooking processes (leaching, boiling, and heating are known to cause nutrient losses to variable degrees). However, by preparation of many of these recipes in the institute's kitchen, standardization was achieved for staple foods contributing to a majority of the variance in energy and nutrient intakes. The creation of a reliable database for staple Indian cooked foods from different regions is necessary to standardize nutrient intakes across different studies.

The dialysis-specific FFQ correlated significantly with the 2-day dietary recall method and provided valid estimates of most nutrients when tested against the dietary recall method. The Bland-Altman plots showed higher estimates for energy as well as the other nutrients of interest by the FFQ method than by dietary recall. It will be necessary to further validate the FFQ using biomarkers to rule out the relative effects of recall-related under-estimation and FFQ-related over-estimation. Also, the cross-classification analysis showed that the FFQ could correctly classify patients into the exact or adjacent quintiles in 56–76% of cases for all nutrient intakes, and gross misclassification rates were 5% or less with an average κ_w of 0.17.

The adjusted protein intake by the 2-day recall method (also by the FFQ) in the study population is 50 g/day, which corresponded to a mean protein intake of 0.97 g/kg and that protein contributed to around 13% of the cumulative energy intake, which is far lower than the recommended protein intake for patients on hemodialysis.[14] During the initial months after hemodialysis initiation, a lag time to adapt from a protein-restricted to a protein-enriched diet may be expected. However, as all the patients in the present study were on hemodialysis for longer than 12 months, alternative explanations should be sought. Poor adherence to prescribed diets, economic constraints, caregiver burden, and lack of repeated contact with dieticians could contribute to low-protein intakes in our study participants. Mean serum albumin level was 3.6 g/dL in our study and values less than 3.7 g/dL have been associated with increased cardiovascular and all-cause mortality in previous studies.[15] Low BMI and low midarm circumference as seen in our study are also indirectly indicative of the poor protein-energy status in these patients.

The mean daily calcium intake (energy-adjusted) in our study by dietary recall was 518 mg/day and by FFQ was 750 mg/day. However, as the mean iPTH in the population was within the target range set by KDIGO and the prevalence of secondary hyperparathyroidism was around 14%, alongwith serum calcium in the normal range, it is likely that our patients are in negative or neutral calcium balance. With regard to phosphorus intake, it is often assumed that Indian diets would not cause excessive phosphorus loads being predominantly vegetarian, low in protein and preservatives, and low in bioavailable phosphorus, however, no previous studies have confirmed or refuted these assumptions. The mean value of 1243 mg/day from the dietary recall and 1557 mg/day from the FFQ were both higher than the recommended 800–1000 mg/day to maintain neutral phosphorus balance in patients on three-per-week hemodialysis. In our center (as in a majority of Indian centers), all the patients are on a twice-weekly hemodialysis schedule, and the hyperphosphatemia seen in our patients (mean serum phosphate of 5.3 mg/dL) could be secondary to the imbalance between inadequate phosphorus removal and high phosphorus diet. A medium-sized roti, the primary staple source of energy intake among the north Indian population provides as much energy as three slices of white bread (as standardized in our kitchen), but with the additional burden of 80 mg of phosphorus content.[16] Dals, one of the primary protein sources (even in patients who consume eggs and meat), have phosphorus to protein ratios ranging from 14 to 18 mg/g (though with lower bioavailability). This finding of a relatively high phosphorus intake in our study needs to be confirmed with larger sample size, as it has important clinical and therapeutic implications. Finally, the mean potassium intake (energy-adjusted) by the dietary recall method was 2517 mg/day (64 mEq/day) and by the FFQ it was 3172 mg/day (81 mEq/day). This is higher than the guideline recommendation of reducing potassium intake to less than 2000 mg/day or 1 mEq/kg/day.[17] Again, the two-per-week dialysis sessions and the unaffordability of daily potassium binder therapy in our resource-limited setting, probably account for a substantial number of patients with hyperkalemia.

The strengths of the present study are the inclusion of a homogenous study population, with a reasonably long dialysis vintage and similar socioeconomic backgrounds. The interviewer-administered FFQ in the local language with visual aids to assess portion size is relatively easy for patients with low literacy to complete. Inclusion of 2 days, one predialysis and one dialysis day is important, as many patients complain of poor appetite on the day preceding hemodialysis and many others eat different foods on the dialysis day than on nondialysis days. These variations could be captured better with 2-day recall. The FFQ developed in the study provided valid estimates of intakes of energy and nutrients of interest, and though only semiquantitative, can still be used successfully to classify patients into groups based on their intakes. The study also provided important information regarding protein, calcium, phosphorus, and potassium intakes in a typical Indian dialysis population, which, to our knowledge, has never been studied before. There are important limitations to our study. We did not test our FFQ for reproducibility. Testing for reproducibility helps eliminate interviewer bias and also captures seasonal variations in FFQ responses (recall bias, consumption of special foods during major festivals). However, we avoided the FFQ administration for a week following festivals. Another limitation is the absence of a nutrient database for cooked foods in India and accounting for the loss of phosphorus and potassium through cooking processes. Nevertheless, as the high intakes of these nutrients were coupled with high serum concentrations as well, they probably represent valid estimates of the true values.

Therefore, our findings of protein-poor, high phosphorus and potassium intake in the study population have important clinical implications. There is a need to design novel dietary interventions and education programs to improve the nutritional status of these socially and economically disadvantaged patients in resource-limited settings. The reinforcement of cooking techniques such as leaching and boiling and periodic counseling to foods high in phosphorus-to-protein ratio needs to be done at regular intervals.

Conclusion

This study describes the development and validation of a novel semiquantitative FFQ for use in a north Indian CKD population on hemodialysis. This FFQ can be used to provide valid estimates of energy and select nutrients of the nephrologist's interest. Using data from the dietary recall of 2 days and FFQ, it can be concluded that the study population consumes a low protein, and high phosphorus and potassium-containing diet, and further nutritional interventions should specifically target these shortcomings.