Food insecurity is associated with high body weight amongst women, but not men, in high-income countries. Previous research using food recalls suggests that the total energy intake of food-insecure women is not elevated, though macronutrient composition may differ from that of food-secure women. There is limited evidence on temporal patterns of food consumption. Here, we used food recalls from women in the 2013-4 cycle of the National Health and Nutrition Examination Survey (NHANES,
Food insecurity (FI)—defined as limited or uncertain access to adequate food—is robustly associated with overweight and obesity amongst women, but not men, in high-income countries [
In humans, studies based on participant-reported intake, usually in the form of 24 hr food recalls, have generally concluded that total energy intake does not differ systematically between women currently experiencing FI and those who are not [
However, the food consumption of women experiencing FI may differ in other ways than just total energy intake. Using data from the National Health and Nutrition Examination Study (NHANES), Bergmans et al. [
This evidence raises the possibility that FI may be associated with subtle changes in the temporal pattern of food intake, even if not the total amount, and this may be relevant to the high body weights observed in women who experience FI. There has been a small amount of prior research on this question. Zizza et al. [
Here, we investigated in detail the 24 hr food-consumption recalls of adult women in the 2013-4 cycle of NHANES. Like previous studies, we extracted variables concerning total energy intake, macronutrient composition, and number of eating occasions in the day. Going beyond previous research, we characterised variability over time within each food recall. Temporal variability is of two kinds: intraday (for example, the variation in time gap or energy intake between the meals of a day) and interday (for example, eating more, or more often, on some days than other days). Having developed our set of variables characterising patterns of food consumption, we tested which ones differed between individuals who did and did not report recent experience of FI, both with and without adjustment for sociodemographic characteristics. We then went on to test whether any of the variables that differed by FI status were significant statistical mediators of the FI-BMI relationship. Our general predictions were that, compared to food security, FI would be associated with no greater total energy intake, but greater reliance on carbohydrate and less consumption of fibre; fewer meals in the day; greater intra- and interday variability in consumption pattern; and a later time of first consumption. We focussed on the women, as it is only in women that an association between FI and body weight is found. We report the parallel analyses for the men in the Supplementary Materials. Those analyses may shed light on why the FI-body weight association is lacking in men.
NHANES is an ongoing multistage survey administered by the National Center for Health Statistics. In each two-year cycle, a large diverse sample of the noninstitutionalized US population is recruited to complete a number of questionnaire and examination measures. The sample can be made nationally representative by the application of sampling weights, as is done here (unweighted results are essentially identical). For our main analysis, we selected all adult (18+ years) participants from the 2013-4 cycle who had completed the questionnaire measures and physical examination (
FI was measured using the adult questions of the standard USDA questionnaire [
Participants completed two separate food recall interviews, the first in person and the second by telephone. Each recall concerned consumption over the 24 hours of the day prior to the interview. The time between the two recall days was 3–10 days. Where appropriate, we averaged the two recall days for participants with both days complete (
We extracted variables algorithmically from the food recall files. Foods and beverages consumed are structured in the recall files by consumption event (CE), each CE representing a unique time in the day when something was consumed. Table
Variables extracted from the food recalls.
Variable name | Definition | Units | Women’s mean (sd) | |
---|---|---|---|---|
Consumption amounts | Energy intake | Total energy intake per day | kcals | 1779 (704) |
Relative carbohydrate | Relative carbohydrate | g | 2.61 (30.20) | |
Relative protein | Relative protein | g | −3.26 (19.93) | |
Relative fat | Relative fat | g | 0.43 (18.76) | |
Relative fibre | Relative fibre | g | 0.23 (6.60) | |
|
||||
Intraday pattern | First CE | Time of first CE | Hours from midnight | 7.93 (2.27) |
Number of CE | Number of CEs per day | Number | 5.57 (1.63) | |
Mean foods per CE | Mean number of distinct foods per CE | Number | 9.68 (3.28) | |
Variability foods per CE | Intraday standard deviation number of distinct foods per CE | Number | 5.42 (1.93) | |
Variability time gap | Intraday standard deviation in time gap between CEs | Minutes | 104.27 (48.79) | |
Variability energy per CE | Intraday standard deviation Kcals per CE | kcals | 322.2 (152.65) | |
|
||||
Interday variability (participants with 2 days of data) | IDD energy intake | Interday difference in energy intake | kcals | 627.91 (577.70) |
IDD first CE | Interday difference in time of first CE | Hours | 1.65 (2.15) | |
IDD number of foods | Interday difference in number of foods | Number | 4.61 (3.84) | |
IDD number of CEs | Interday difference in number of CEs | Number | 1.48 (1.32) | |
IDD mean time gap | Interday difference in mean time gap between CEs | Minutes | 63.42 (70.67) |
CE: consumption event. IDD: interday differences (for participants with two separate days of food recall data).
We did not include variables that were completely predicted by combinations of other variables. For example, the mean time gap between CEs is completely predicted by the time of first CE and the number of further CEs in the day. Hence, it was not necessary to include it separately in the set of variables.
For our main analyses, we used multivariate analyses of variance (MANOVAs) to examine whether food-secure and food-insecure women differed on each of three sets of food-consumption variables. The sets of variables were: consumption amounts (5 variables concerning total energy intake and macronutrient composition); intraday pattern (6 variables concerning diversity of foods and variability of consumption within a day); and interday variability (5 variables concerning how the two recall days differed from one another). For each set of outcome variables, we performed both a simple and an adjusted MANOVA. For the simple MANOVAs, the sole predictor was FI. For the adjusted MANOVAs, we additionally included control variables: age (years), income (% of federal poverty line, NHANES variable INDFMPIR), education (NHANES variable DMDEDUC2), ethnicity (NHANES variable RIDRETH1), and presence of children in the household (from NHANES variables DMDHHSZA and DMDHHSZB). To follow up significant MANOVA results and understand which variables in each set were driving any overall differences, we then performed univariate general linear models on each outcome variable separately.
Having established which food-consumption variables were significantly predicted by FI after adjustment, we then tested whether any of them predicted BMI, adjusting for income, age, education, and ethnicity. Variables that were both predicted by FI and predicted BMI were considered candidate mediators of the FI-BMI association. To test the extent of mediation, we used R package “lavaan” [
Descriptive statistics for the main food-consumption variables are shown in the final column of Table
Key results are summarised in Table
Parameter estimates for the difference between food-secure and food-insecure women. Adjusted models include income, education, ethnicity, having children in the household, and age as additional predictors. Food-secure is the reference category, and hence the parameter estimates represent the deviation of food-insecure women from the food-secure mean.
Unadjusted | Adjusted | |||
---|---|---|---|---|
|
|
|
| |
|
MANOVA F(5, 2792) = 21.52 | <0.001 | MANOVA F(5, 2579) = 36.32 | <0.001 |
Energy intake | 6.74 (29.55) | 0.82 | −5.79 (34.33) | 0.87 |
Relative carbohydrate | 9.92 (1.34) | <0.001 | 4.30 (1.55) | 0.006 |
Relative protein | −4.50 (0.99) | <0.001 | −2.63 (1.04) | 0.01 |
Relative fat | −3.22 (0.81) | <0.001 | −0.88 (0.94) | 0.35 |
Relative fibre | −2.02 (0.28) | <0.001 | −0.80 (0.31) | 0.01 |
|
||||
|
MANOVA F(6, 2685) = 24.68 | <0.001 | MANOVA F(6, 2482) = 27.67 | <0.001 |
First CE | 0.25 (0.09) | 0.007 | −0.14 (0.11) | 0.20 |
Number of CEs | −0.50 (0.07) | <0.001 | −0.12 (0.08) | 0.15 |
Mean foods per CE | −1.49 (0.14) | <0.001 | −0.43 (0.16) | 0.006 |
Variability foods per CE | −0.90 (0.09) | <0.001 | −0.29 (0.10) | 0.002 |
Variability time gap | 16.15 (2.07) | <0.001 | 9.61 (2.39) | <0.001 |
Variability energy per CE | 22.07 (6.48) | <0.001 | 10.40 (7.47) | 0.16 |
|
||||
|
MANOVA F(5, 2516) = 8.96 | <0.001 | MANOVA F(5, 2327) = 9.31 | <0.001 |
IDD energy intake | 65.15 (25.91) | 0.01 | 17.77 (30.51) | 0.56 |
IDD first CE | 0.52 (0.09) | <0.001 | 0.28 (0.11) | 0.01 |
IDD number of foods | 0.05 (0.18) | 0.79 | 0.33 (0.21) | 0.12 |
IDD number of CEs | 0.11 (0.06) | 0.06 | 0.15 (0.07) | 0.03 |
IDD mean time gap | 13.32 (3.26) | <0.001 | 4.87 (3.78) | 0.20 |
For the six variables concerning intraday patterning of consumption, there was a significant difference between the food-secure and food-insecure women overall in the unadjusted analysis. This was driven by food-insecure women having their first CE later; having fewer CEs in the day; fewer distinct foods per CE; a less variable number of distinct foods per CE; more variable time gaps between CEs; and more variability in energy per CE. The overall significant difference between food-secure and food-insecure women persisted in the adjusted analysis. Amongst the individual variables, the differences in time of first CE, number of CEs, and variability in energy per CE were attenuated to the point of nonsignificance by the adjustment. Thus, after adjustment, significant differences between food-insecure and food-secure women persisted in the mean and variability of foods per CE and the variability of the time gap between CEs. These three variables also differed significantly with FI status using the four-level classification of FI, again showing gradients of severity, with the severest FI producing the most extreme means (Supplementary Table
For the variables based on interday differences in pattern, the effect of FI in the MANOVA was significant both adjusted and unadjusted. In the unadjusted analysis, food-insecure women differed from food-secure women by having greater interday difference in total energy intake; greater interday difference in the time of the first CE; greater interday difference in the number of CEs; and greater interday difference in the mean time gap between CEs. After adjustment, only the interday difference in the time of the first CE and the interday difference in the number of CEs remained significantly associated with food insecurity. Both of these associations remained significant using the four-level classification of FI, though they lacked clear evidence of severity gradients (Supplementary Table
To visualize the results and establish which variables were most strongly associated with FI, we standardized parameter estimates from all of the adjusted univariate analyses and produced a forest plot (Figure
Forest plot of standardized associations between food insecurity status and food consumption variables for NHANES women after adjustment for age, income, education, ethnicity, and presence of children in the household. Variables are sorted so that those more strongly associated with food insecurity status appear higher on the figure. A negative value indicates that food-insecure women have a lower value of the parameter, and a positive value a higher value. Whiskers represent 95% confidence intervals. CE: consumption event. IDD: interday difference (for participants with two separate days of recall data).
Food-insecure women had higher BMIs than food-secure women (insecure: mean 31.13, sd 8.86; secure: mean 28.77, sd 7.37). This constituted a significant difference after adjustment for income, education, ethnicity, age, and presence of children in the household (
We explored whether the food-consumption variables we had identified as robustly associated with FI could serve as mediators of the association between FI and BMI. We ran models testing whether each of the eight variables with parameter estimates significantly different from zero in Figure
Results of models testing whether each of the food consumption variables significantly associated with food insecurity predicts body mass index in NHANES women. All models are adjusted for age, income, education, ethnicity, and presence of children in the household.
Predictor |
|
|
---|---|---|
Relative carbohydrate | −0.01 (0.005) | 0.06 |
Relative protein | 0.01 (0.005) | 0.05 |
Relative fibre | −0.12 (0.03) | <0.001 |
Mean foods per CE | −0.11 (0.05) | 0.02 |
Variability foods per CE | 0.01 (0.08) | 0.87 |
Variability time gap | 0.01 (0.003) | 0.004 |
IDD first CE | 0.01 (0.08) | 0.92 |
IDD number of CEs | 0.02 (0.12) | 0.86 |
We then created a multiple mediation model with BMI as the outcome, FI as the predictor, and variability time gap, mean foods per CE, and relative fibre consumption as the mediators. There was an overall positive effect of FI on BMI (total effect 2.21, se 0.33,
Using 24 hr food recalls from participants in the large, nationally representative NHANES survey, we found that total energy intake was no higher in women classified as food-insecure than in women classified as food-secure. However, patterns of food consumption differed in many other ways. Specifically, food-insecure women had more variable time gaps between eating; ate a smaller and less variable number of distinct foods at a given consumption event; were more variable from day to day in their time of first consumption in the day; were more variable from day to day in the number of times they ate; and consumed relatively more carbohydrate, less protein, and less fibre. These differences were robust to control for age, income, education, ethnicity, and the presence of children in the household. Moreover, we showed in supplementary analyses that most of these variables exhibit clear gradients of severity when FI is divided up into finer categories. Thus, food-insecure women eat a diet that is less diverse than that of food-secure women, but do so in a more temporally variable way. We found that three of the food-consumption differences between food-insecure and food-secure women—their more variable time gaps between eating, their lower number of distinct foods per consumption event, and their lower fibre consumption—partially accounted for their greater body masses.
These findings are informative on several different levels. At the simplest level, they can be seen as a validation of the FI questionnaire measure. We can detect, in the detailed food recalls, that women classified as food-insecure had more variable gaps between meals and relied on a smaller number of foods (and these differences became larger as the severity of their FI increased). The relatively high carbohydrate composition and low protein and fibre composition of food-insecure women suggest a reliance on cheap sources of calories and low consumption of vegetables, fruit, and dairy. This pattern would be expected where budgets for obtaining food are highly constrained [
More deeply, the findings bear on the question of how experiencing FI may lead to high body weight in women in developed countries. The results here concur with those of similar investigations [
If total energy intake does not increase in response to experiencing FI, this would not undermine the general principle that weight gain is an adaptive response to FI [
Although we found some support for the contention that differences in food-consumption patterns statistically mediate the association between FI and body weight in women, the extent of the mediation was weak. Between them, the three mediating variables accounted for less than 15% of the association between FI and body weight. At face value, this implies we largely failed to identify what it is that makes women who experience FI become heavier than those who do not. However, the mediation pathways we identified may be more important than the 15% figure suggests.
The reasons for this relate to sources of measurement error in the design, beyond the reliance of the dietary recalls on participant report. Measurement error generally leads to underestimation of associations and may well have this effect in the current study. The FI questionnaire asked about experiences of FI in the last 12 months. Positive responses to items on the questionnaire thus indicate that FI had been experienced recently, but not necessarily that it was still being experienced on the days of the dietary recalls. We can be confident that our “food-secure” group was not experiencing FI on the day of the recalls (since they had not experienced FI at all within the last 12 months), but our “food-insecure” group probably consisted of a mixture of people who were currently experiencing FI and those who had experienced FI, but whose situations had recently improved. In this respect, we believe our design to be conservative. The true differences between average dietary behaviour of people currently being affected by FI and those currently not must be at least as large, if not larger, than those observed between our “food-secure” and “food-insecure” groups.
Second, food-consumption variables here are based on a maximum of two days of data for each participant. Given we have a lot of replication between individuals, then as long as the recall days are a fairly random sample of all days, this still allows high power to detect systematic patterns of difference in the distribution of dietary behaviours between people experiencing FI and those who are not. However, the sampling variability of which days the recalls happened to fall upon is an additional source of noise and hence measurement error. This is more of an issue for the interday difference variables, for which there is no within-person replication, than for the within-day variables, for which there is one within-person replicate.
Given these measurement-error issues, we would expect the measured associations between FI and food-consumption variability, and also between food-consumption variability and body weight, to be underestimates of the true associations. Viewed in this light, rather than seeing it as a shortcoming that we can only account for 15% of the FI-body weight relationship, we find it noteworthy that from just two days of food recalls, we can detect numerous significant differences between women who do and do not report recent experience of FI, and that some of these statistically mediate any of the excess body weight associated with FI.
Nonetheless, there are likely to be important differences between food-insecure and food-secure women not captured in our set of food-consumption variables. We have included no measures relating to physical activity. Though a physical activity questionnaire was administered in this cycle of NHANES, the estimation of metabolic equivalents from the questionnaire responses is indirect. Moreover, physical activity estimated from similar questionnaires is generally poorly correlated with physical activity as objectively measured using accelerometers [
Change in energy intake and change in physical activity do not exhaust the possible pathways through which FI could lead to weight gain. Birds, for example, are able to change both their digestive efficiency and overnight metabolic rate in response to changes in food availability [
Our findings do not provide a clear picture of why FI leads to high body weight in women but not men. Patterns of food consumption differed between food-insecure and food-secure individuals among men in very similar ways to women (see Supplementary Materials, Section
In a large, nationally representative US sample, we have shown that experience of FI, as measured by the USDA questionnaire, corresponds to measurably different patterns of food consumption. In line with previous studies, we found that food-insecure women eat more carbohydrate and less protein and fibre, but appear to consume the same amount of energy overall. We also showed that they ate a lower diversity of foods, and, critically, that they showed greater temporal variability in their intake. These variations in food-consumption patterns may be part of the reason that women who experience FI end up with higher body weights.
The NHANES 2013-4 data are downloadable from
The authors declare that they have no conflicts of interest.
This project received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 Research and Innovation Programme (grant agreement no AdG 666669, COMSTAR).
Table S1: women’s estimated marginal means and standard errors by the food insecurity category, using the four-level classification of the USDA score rather than the dichotomous classification of food-secure versus food-insecure. Table S2: parameter estimates for the difference between food-secure and food-insecure men. Adjusted models include age, income, education, ethnicity, and presence of children in the household as additional predictors. Food-secure is the reference category, and hence the parameter estimates represent the deviation of food-insecure men from the food-secure mean. Table S3: results of models testing whether each of the food consumption variables significantly associated with food insecurity predicts body mass index in NHANES men. All models are adjusted for age, income, education, ethnicity, and presence of children in the household. Figure S1: forest plot of standardized associations between food insecurity status and food consumption variables for NHANES men after adjustment for age, income, education, ethnicity, and presence of children in the household. Variables are sorted so that those more strongly associated with food insecurity status appear higher on the figure. A negative value indicates that food-insecure women have a lower value of the parameter, and a positive value a higher value. Whiskers represent 95% confidence intervals. CE: consumption event. IDD: interday difference (for participants with two separate days of recall data).