Introduction

Dietary reference intakes (DRIs) have been a cornerstone of United States (US) nutrition policy since 1943 [1]. They impact federally funded nutrition programs and, the recommended population level of sodium can elicit polarizing responses from scientists [2, 3], industry representatives [4, 5], and journalists [6, 7]. Sodium is a nutrient that has been highly debated with persuasive and elegant arguments made for both population sodium reduction [8, 9] and for maintenance of the status quo [10, 11].

After the 2015 Dietary Guidelines Advisory Committee (DGAC) report was published [12••], controversy ensued, and by Congressional mandate, the sodium DRIs were updated in 2019 [13••]. The 2019 adequate intake (AI) levels by age–sex groups are largely consistent with the DRIs published in 2005. The areas of difference are as follows:

  • AI of 100 mg/day for infants 0–6 months (decreased from 2005)

  • AI of 800 mg/day for children 1–3 years (decreased from 2005)

  • AI of 1000 mg/day for children 4–8 years (decreased from 2005)

  • AI of 1200 mg/day males and females 9–13 years (decreased from2005)

  • AI of 1500 mg/day for adults 51–70 years and adults >70 years (increased from 2005)

In the 2019 DRIs, the tolerable upper level (UL) was no longer used for sodium. This is a point of difference from the DRIs published in 2005 [14] and is based on a revision in methodology such that ULs are driven by toxicological responses [15]. In 2019, instead of ULs for sodium, the chronic disease risk reduction (CDRR) DRIs were set [13••]. The CDRR DRIs are a new feature of sodium DRIs. CDRR is the intake level at which reduction in intake is expected to reduce chronic disease risk within an apparently healthy population. The differences between the UL set in the 2005 DRIs and the CDRR set in the 2019 DRIs are as follows:

  • a CDRR of 1200 mg/day for children 1–3 years (decrease from 2005 UL)

  • a CDRR of 1500 mg/day for children 4–8 years (decrease from 2005 UL)

  • a CDRR of 1800 mg/day for males and females 9–13 years (decreased from 2005 UL)

Given the overall similarities between the 2005 and 2019 DRIs, one may wonder how recently published research on sodium and health outcomes was considered in determining the DRIs, particularly the recent studies from very large observational cohort studies [16,17,18,19,20,21]. And, it may even raise questions as to whether the recommendations from the 2019 committee were simply replicating previous knowledge, or whether it was driven by a lack of certainty in the newly published results.

To answer these questions, it is important to consider that epidemiologic research on sodium and health has sought answers to causal questions such as “will decreased sodium intake reduce risk of cardiovascular disease (CVD)? If so, by how much and for what populations?” Often, these answers can contribute to establishing nutrition guidelines and associated policies, which will subsequently improve population health. When DRIs are being determined, a consensus panel of scientists systematically reviews the literature to evaluate certainty in the presented results and weigh the individual studies based on the potential for bias [22, 23, 24•, 25•]. Additionally, the DRI committee considers the magnitude and direction of the potential bias and discusses the likelihood that the studies’ conclusions would meaningfully change in the absence of bias. They consider findings from all study designs and must often grapple with the paucity of randomized clinical trials and some inconsistency across the observational epidemiologic studies. Moreover, in epidemiology, there has been a shift towards the use of very large datasets to understand exposure–disease relationships, and the use of larger sample sizes is sometimes misinterpreted as confidence in the results obtained. Although a benefit of using very large datasets is improved precision in effect estimation, this does not indicate that these data will yield valid estimates of the relationship between exposure and outcome [26].

Herein, we aim to outline major threats to ascertaining valid estimates of the relationship between dietary sodium and health outcomes in observational cohort studies. We use tools from modern epidemiology to demonstrate how unexpected and inconsistent findings in these relationships may emerge.

Current Challenges in Estimating Relationships Between Dietary Sodium and Health Outcomes

In observational studies, the main analyses estimate statistical associations between dietary sodium intake and a specific health outcome. Inferring causation from these statistical associations is a difficult task and requires strict assumptions [27]. Nutritional epidemiology, and particularly the study of specific micronutrients, has been criticized as being plagued with methodological issues limiting this inference [28, 29]. In the case of sodium, these doubts contribute to the debates about the recommended intake level for this nutrient and a lack of confidence, by some, in the guidelines [30].

In Table 1, we discuss assumptions in inferring causation from observational data that are the key to the investigation of the effects of sodium intake on health. We illustrate a few of these assumptions (Figs. 1a–c) using causal directed acyclic graphs (DAGs) adapted from figures presented in a textbook by Hernán and Robins (2020) [27]. In brief, a DAG is a graphical representation of the causal effects between variables. They are constructed from a set of edges (arrows) and nodes (variables) based on a priori assumptions about the causal relations among the exposure, outcome, and covariates. An arrow between two variables implies a direct causal effect. Two variables (i.e., X and Y) may be statistically associated if (1) X directly or indirectly causes Y, (2) X and Y share a common cause (i.e., confounding variable), or (3) a descendent of X and Y (i.e., collider) has been conditioned on [27, 31,32,33].

Table 1 Key assumptions in inferring causation from observational data in the context of sodium intake and CVD
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Fig. 1
figure 1figure 1

a Illustration of potential information bias. b Illustration of potential reverse causality. c Illustration of potential selection bias

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Increasingly, DAGs are being used to help depict different causal structures, thus forcing researchers to be explicit about the research question and the underlying assumptions about how the variables of interest are related. This approach facilitates communication within the research community giving us a framework around which we can align. Additionally, DAGs facilitate appropriate selection of covariates for regression analyses and help elucidate potential sources of bias.

Discussion and Conclusions

We aimed to outline major threats to ascertaining valid estimates of the relationship between dietary sodium and health outcomes in observational cohort studies. We use directed acyclic graphs to illustrate specific examples in which biases may occur. These are tools that can be used throughout the research process to inform which variables should be measured in research studies, what variables should be adjusted for in our multivariable analyses, and how the procedures used to select participants into studies affect internal validity of study results. They can also be used alongside bias quantification methods [22, 23, 24•, 25•] to estimate the magnitude and the direction of the bias present.

The key threats to internal validity we have identified in this paper are as follows:

  • poorly defined target intervention

  • poorly measured sodium exposure

  • unmeasured or residual confounding

  • reverse causality

  • selection bias

Researchers should consider these threats to internal validity while developing research questions and throughout the research process. A well-defined question with a clearly articulated target intervention can be more easily translated to nutritional policy. Other threats to validity can be eased during the study design process. For example, using multiple modes of sodium measurement such that findings can be contrasted within the same study sample will inform the extent to which measurement error is biasing results. Bias due to confounding and reverse causality can be eased by measurement of auxiliary variables. Developing a DAG in collaboration with subject area experts can be used to identify which variables need to be measured and then subsequently adjusted for in statistical analyses.

We also highlight the importance of clearly defining a target population for which the study results should generalize to. Threats to external validity, too, have implications for nutritional policy makers. Studies of sodium and disease in clinical and high-risk populations are beneficial in understanding physiologic mechanisms at play as well as targeted interventions for these groups. These studies should not be prioritized, however, in informing national dietary guidelines—which are focused on establishing recommendations for health promotion and disease prevention across the US. It is imperative that observational research informing national guidelines includes representation of all population subgroups and that the study population is representative of the general US population.

Conclusions

Despite strong opinions about the usefulness of nutritional epidemiology [29, 36, 37], and the labeling of this field as flawed [29], it may be more productive and informative to think through how the limitations of the methods employed in these studies affect their conclusions. This can guide us to understand the implications of published analyses, regardless of the size of the dataset, and help inform well designed studies that can be used to set sodium policies.