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. 2021 Jan 29;23(3):481–487. doi: 10.1177/1099800420985615

Longitudinal Assessment of Relationships Between Health Behaviors and IL-6 in Overweight and Obese Pregnancy

McKenzie K Wallace 1,✉, Nitin Shivappa 2,3, Michael D Wirth 2,3,4, James R Hébert 2,3, Larraine Huston-Gordesky 5, Fernanda Alvarado 6, Sylvie Hauguel-de Mouzon 7, Patrick M Catalano 5,7
PMCID: PMC8755950  PMID: 33511855

Abstract

Background:

Inflammation is a common factor in adverse pregnancy outcomes (APOs). Behavioral factors influence inflammatory markers and APOs but rarely have been investigated simultaneously in pregnancy. Our purpose was to determine how diet, physical activity, and obesity are associated with interleukin (IL)-6 in early and late pregnancy.

Methods:

We conducted a secondary analysis of 49 overweight/obese pregnant women. Health behavior data, including diet quality using the Dietary Inflammatory Index (DII®); physical activity (Leisure Time Physical Activity scale); body mass index (BMI); and plasma IL-6 concentrations were collected at 13–16 weeks (early pregnancy) and 34–36 weeks (late pregnancy) gestation. Multiple linear regression analyses were used to determine the amount of variance explained in early and late pregnancy IL-6 concentrations by early and late pregnancy diet, physical activity, and BMI.

Results:

Early diet and early BMI were the strongest predictors of early IL-6 concentrations (R2 = 0.43; p < .001) and late IL-6 concentrations (R2 = 0.30; p < .001). Late BMI predicted late IL-6 (R2 = .11; p = .02). Change in diet over pregnancy predicted late IL-6 (R2 = 0.17; p = .03).

Conclusion:

These findings suggest that maternal diet and BMI in early pregnancy, which likely reflects prepregnancy status, may have a greater impact on inflammatory processes than factors later in pregnancy. Future work should assess if behavioral factors before pregnancy produce similar relationships to those reported here, which may clarify the timing and type of lifestyle interventions to effectively reduce APOs.

Keywords: diet, physical activity, obesity, inflammation, pregnancy

Inflammation is a multifactorial process influenced by a myriad of biological, behavioral, and metabolic factors (Eisenberger et al., 2017; Hawiger & Zienkiewicz, 2019; Maurizi et al., 2018). Successful pregnancy requires a balanced inflammatory response. As proposed by Kalagiri and colleagues (2016), early pregnancy is dominated by a pro-inflammatory response marked by successful implantation, vascular remodeling, and trophoblast and placental development. A balanced anti-inflammatory response supports fetal growth and development throughout the second and third trimesters. At the end of pregnancy, a pro-inflammatory response triggers cervical dilation and labor onset leading to the birth of the infant. However, an altered, adverse immune response has been implicated in the pathophysiology of adverse pregnancy outcomes (APOs).

Elevated inflammatory markers have been observed during early pregnancy in women who go on to develop gestational hypertension, preeclampsia, gestational diabetes, and preterm labor (Ferguson et al., 2014; Kalagiri et al., 2016; Powe, 2017; Tangeras et al., 2015). These conditions have adverse outcomes for both the mother and infant during pregnancy and birth and lifelong risk for cardiovascular disease, type-2 diabetes, and other chronic metabolic conditions for the offspring, but have limited treatments and clinically meaningful predictive tools (Aviram et al., 2016; Kalagiri et al., 2016; Mol et al., 2016). Hence, researchers have been investigating the use of inflammatory markers as prediction tools for identifying who is at risk for developing APOs.

Inflammatory markers consist of numerous proteins that regulate the innate and adaptive immune response (Hawiger & Zienkiewicz, 2019). Cytokines are among the most studied inflammatory markers in pregnancy, and interleukin-6 (IL-6) is a hallmark pro-inflammatory cytokine frequently examined in relation to adverse pregnancy outcomes. IL-6 is a candidate inflammatory marker researchers are seeking to use to predict who will develop APOs. However, one barrier to using inflammatory markers, such as IL-6, as a predictive tool is the lack of simultaneous investigation of factors known to influence inflammation, such as diet, physical activity, and obesity (Fernandes et al., 2020; Mraz & Haluzik, 2014; Pedersen, 2017). These same health behaviors that influence inflammation also influence the risk of developing APOs (Athukorala et al., 2010; Catov et al., 2018; Schoenaker et al., 2015).

Obesity, diet, and physical activity influence both inflammation and risk for APOs. Overweight or obese individuals are more likely to develop gestational diabetes and hypertensive disorders of pregnancy (Athukorala et al., 2010; Yogev & Catalano, 2009). Obese individuals with higher amounts of fat mass have higher IL-6 plasma concentrations because white adipose tissue directly releases IL-6 in the systemic circulation (Mraz & Haluzik, 2014; Resi et al., 2012). Anti-inflammatory diets such as the Mediterranean diet, which is rich in Omega-3 fatty acids, have been associated with lower risk of developing gestational hypertension (Schoenaker et al., 2015). Diet also influences inflammatory markers; for example, a recent meta-analysis showed increasing olive oil consumption, reduced IL-6 levels (Fernandes et al., 2020). Increased physical activity is associated with lower risk of gestational diabetes and hypertensive disorders of pregnancy (Catov et al., 2018). Research also suggests that although there is an acute increase in IL-6 (released from skeletal muscle) during exercise, the cascade response results in an overall anti-inflammatory response (Pedersen, 2017).

Given the overlap between lifestyle behaviors, inflammatory markers, and pregnancy complications, assessments of the relationships between behavioral factors and inflammation in pregnancy are needed before researchers can use inflammatory markers as predictors of pregnancy complications or design behavioral interventions associated with inflammation to prevent pregnancy complications. Although numerous studies have investigated inflammatory markers in pregnancy and the relationship between singular health behaviors and inflammatory markers, few have simultaneously examined the impact of multiple maternal health behaviors such as diet, physical activity, and obesity on inflammatory markers during pregnancy. Likewise, few have attempted to ascertain which behavioral factors may have stronger relationships with inflammatory markers. To address this gap, this study was undertaken to determine if diet, physical activity, or maternal BMI contributed to variations in concentrations of interleukin-6 (IL-6) in early (13–16 weeks) and late (34–36 weeks) pregnancy in a group of overweight and obese pregnant women.

Materials and Methods

We conducted a secondary analysis of 49 overweight and obese pregnant women recruited from an urban hospital. The parent study was a double-blind, randomized controlled trial (NCT00957476) investigating the effect of Omega-3 fatty acid supplementation (800 mg docosahexaenoic acid (DHA) and 1,200 mg of eicosatetraenoic acid (EPA) on concentrations of inflammatory markers (CRP, IL-6, IL-8) in pregnant women (Haghiac et al., 2015). Measures of diet, physical activity, obesity, and inflammation were collected at baseline (early pregnancy: 13 0/7 to 16 6/7 weeks gestation) and post-intervention (late pregnancy: 34 0/7 to 36 6/7 weeks gestation). The study’s procedures and inclusion criteria have been previously described (Haghiac et al., 2015). Briefly, the intervention group (n = 25) received daily Omega-3 supplements after randomization following the baseline study visit. The control group (n = 24) received placebo capsules (wheat germ oil). At baseline, there were no differences in Omega-3 dietary intake based on the Harvard Willett Food Frequency Questionnaire responses and baseline plasma levels of total Omega-3 fatty acids nor any other clinical features between the control and intervention groups. The Omega-3 intervention also had no impact on insulin resistant or neonatal adiposity, which were also outcomes of the primary study. All other study procedures and clinical care were the same between groups. The control group and intervention groups were combined in the present analysis. The Omega-3 supplement given to the intervention group, which is the only distinction between the intervention and control group, is accounted for in the Dietary Inflammatory Index score (DII®, described below), allowing the groups to be combined. Notable inclusion criteria were singleton pregnancy and pre-pregnancy BMI > 25kg/m2; notable exclusion criteria were fetal anomaly, current use of fish oil supplements, allergy to fish or gluten, daily use of NSAIDs, or prior diagnosis of hypertension, diabetes, or hyperthyroidism. Institutional Review Board approval was obtained and written informed consent was obtained from each participant.

Measures

For this secondary analysis, IL-6 levels were used because there was no significant change in IL-6 levels related to the Omega-3 supplement intervention in the parent study (0.5 pg/mL vs. 0.1 pg/mL; p = .30; Haghiac et al., 2015). All blood samples were obtained via intravenous catheter between 7 am and 9 am after an overnight fast (no food after 11 pm). The narrow window of blood collection reduces the confounding effect of the diurnal nature of IL-6. Samples were stored at −80 °C until all samples were collected and run as a batch analysis. Maternal plasma IL-6 concentrations (pg/mL) were measured by Quantikine ELISA kits (R&D Systems, MN) according to manufacturer instructions with a coefficient of variance of 0.1% to 7.2%. Information about diet was obtained via the Harvard Willett Food Frequency Questionnaire (Willett et al., 1985) and nutrient values calculated from this questionnaire were used to derive the Dietary Inflammatory Index (DII®) Score (Shivappa et al., 2014), as described below. Physical activity levels were measured by the Minnesota Leisure Time Physical Activity Questionnaire (LTPA; Taylor et al., 1978). The LTPA scale score reflects a metabolic equivalent of task value (MET); a higher MET value indicates higher physical activity levels. BMI (weight(kg)/height(m)2) was calculated from height and weight obtained at the study visits. Adiposity was assessed by air densitometry (BOD POD; COSMED, Inc, Rome, Italy), which estimates fat mass and lean mass to the nearest 0.01 kg. Pregnancy-specific formulas for early- and late-pregnancy fat mass, which account for the varying effects of hydration status during pregnancy, were used to calculate the amount of fat mass (Catalano et al., 1995).

Dietary Analysis

Nutrient values were used to calculate the DII scores according to the methods developed by Shivappa and colleagues (2014). The DII has been successfully validated in numerous populations, including pregnant women (Sen et al., 2016; Vahid et al., 2017). Further technical details about the DII score calculation are available in previous publications (Hébert et al., 2019; Shivappa et al., 2014). Briefly, calculation of DII scores was based on a combination of foods, nutrients, and other constituents collectively known as food parameters. Data were available on the following 21 food parameters: carbohydrate; protein; fat; fiber; alcohol; cholesterol; monounsaturated fatty acids; polyunsaturated fatty acids; saturated fatty acids; Omega-3 fatty acids; Omega-6 fatty acids; vitamins A, B6, B12, C, D; caffeine; energy; iron; magnesium; niacin. Briefly, to calculate the DII score, each food parameter’s intake is converted to a z-score using the reference database (Shivappa et al., 2014). The z-scores for each food parameter are converted to a centered proportion, then multiplied by the respective inflammatory scores, and summed to obtain the overall DII score for each participant (Shivappa et al., 2014). To control for the effect of total energy intake, energy-density, DII (E-DII™) scores were calculated per 1,000 calories of food consumed. A higher E-DII score represents a diet with greater pro-inflammatory potential, while a lower E-DII score represents a diet with greater anti-inflammatory potential.

Because the Omega-3 supplementation would influence the E-DII score, as Omega-3 fatty acids is one of the food parameters, the late-pregnancy E-DII scores for the intervention group, but not the control group, were calculated including the Omega-3 supplement (800 mg DHA and 1,200 mg EPA). The early-pregnancy E-DII score does not contain the Omega-3 supplement because measures were collected before supplementation. Including the Omega-3 supplement in the late-pregnancy E-DII score allows the intervention group and control group to be combined for the purposes of our analyses.

Statistical Analysis

Analyses were run using SPSS version 25 (IBM SPSS® Statistics for Macintosh, Version 25.0, Armonk, NY). Descriptive statistics, including means and standard deviations and normality measures, were assessed for all study variables. To account for outliers in the early pregnancy physical activity data, while maintaining the sample size, the early pregnancy physical activity data were winsorized (Winsor approach) so that the bottom fifth percent of data were set equal to the fifth percentile, and the top fifth percent of data were set equal to the 95th percentile for early physical activity (Field, 2013). The Winsor approach is a validated tool for addressing excessive influence of outliers in regression analysis without completely removing potentially relevant data from the analysis (Pusparum et al., 2017). Paired samples t-tests were conducted to examine if physical activity, E-DII score, BMI, or IL-6 concentrations changed between early-pregnancy and late-pregnancy.

Multiple linear regression analysis was used to determine if early, late, and absolute change (late-pregnancy subtracted by early-pregnancy) values for diet (E-DII), physical activity, and BMI predicted IL-6 concentrations in early- and late-pregnancy. Four separate models were assessed: Model 1) early-pregnancy factors with early-pregnancy IL-6; Model 2) early-pregnancy factors with late-pregnancy IL-6; Model 3) late-pregnancy factors with late-pregnancy IL-6; Model 4) change-over-pregnancy factors with late-pregnancy IL-6. Assumptions of regression analysis were met for each model. Due to the small sample size, early, late, and change over pregnancy factors and late-pregnancy IL-6 could not be combined into a single model. The four models were run as separate multilinear regression analyses with non-significant pathways (p > .05) removed to create the final models shown below.

Results

Table 1 describes the demographic characteristics. The participants (N = 49) were mostly multiparous, had an average age of 27 ± 5.1 years, BMI of 32 ± 6.1 kg/m2, and gestational weight gain of 20.5 ± 12.8 pounds). Leisure-time physical activity levels (LTPA) were significantly higher in early compared to late-pregnancy (p = 0.02). Maternal BMI was significantly higher in late compared to early-pregnancy (p < .001). E-DII score, fat mass (kg), and maternal plasma IL-6 concentrations were not significantly different between early and late-pregnancy (Table 2).

Table 1.

Maternal Characteristics.

N (%) Mean (SD)
Age 27.1 (5.1)
Baseline BMI 32.4 (6.1)
Weight gain in pregnancy (lbs) 20.5 (12.8)
Parity = 0 12 (24.5)
Parity ≥ 1 37 (75.5)
Race
(white)		 21 (42.9)
 (African American) 17 (34.7)
 (other) 11 (22.4)
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BMI = body mass index.

Table 2.

Paired Samples T-Test Results: Difference Between Early- and Late-Pregnancy.

Variable Name Early Late p-Value
Body Mass Index (BMI) 32.4 (6.1) 35.9 (5.6) <.001
Fat Mass (kg) 35.8 (11.8) 37.8 (11.0) 0.27
Energy-adjusted Dietary Inflammatory Index (E-DII) −1.1 (1.2) −1.3 (1.4) 0.17
Physical Activity (METs) 145.6 (141.3) 79.7 (125.7) 0.02
Interleukin-6 (pg/mL) (IL-6) 2.0 (1.5) 2.4 (1.2) 0.79
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Table 3 reports the regression model results for early-pregnancy E-DII (diet), early-pregnancy physical activity, and early-pregnancy BMI as predictors of early-pregnancy IL-6 concentrations (Model 1 as described above). Early-pregnancy E-DII and BMI, but not physical activity, collectively explained about 43% of the variance of early-pregnancy IL-6 concentrations (F (2,44) = 18.17, p < 0.001; adjusted R2 = 0.43). The unstandardized betas show that for every one-point increase in BMI, IL-6 concentrations increased by 0.13 pg/mL, and for every unit increase in the E-DII score, IL-6 concentrations increased by 0.35 pg/mL. The standardized betas indicate that early-pregnancy BMI was the strongest predictor (β = .56) and diet second (β = .28) for early-pregnancy IL-6 concentrations.

Table 3.

Predictors of Early-Pregnancy IL-6 Concentrations (Model 1).

Study Variable B SE(B) β t p-Value
Model 1*
Early E-DII1 .35 .14 .28 2.45 .01
Early BMI2 .13 .03 .56 4.92 <.001
R2 = .43; F (2,44) = 18.17, p < 0.001
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Note. *Early-pregnancy physical activity was not included because non-significant and was removed from the model.

1Early-pregnancy energy-adjusted Dietary Inflammatory Index score.

2Early-pregnancy BMI.

Table 4 reports regression models of E-DII, BMI, and LTPA in early pregnancy (Model 2), late pregnancy (Model 3), and change over pregnancy (Model 4) as predictors of late pregnancy IL-6 concentrations. In Model 1, early-pregnancy E-DII score and early-pregnancy BMI explained about 30% of the variance in late-pregnancy IL-6 concentrations (F (2,44) = 10.78, p < 0.001; adjusted R2 = 0.30). The unstandardized betas show that late IL-6 concentrations increased by 0.06 pg/mL for every one-point increase in early-pregnancy BMI. For every unit increase in the early-pregnancy E-DII score, late IL-6 concentrations increased by 0.44 pg/mL. The standardized betas show early diet was a stronger predictor of late IL-6 (β = .44) than early pregnancy BMI (β = .29). In the late-pregnancy model (Model 3), late-pregnancy BMI was the only predictor of late-pregnancy IL-6 concentrations, explaining about 11% of late-pregnancy IL-6 concentrations (F (1,46) = 6.56, p = .02; adjusted R2 of 0.11). In the change-over-pregnancy model (Model 4), absolute change in E-DII scores from early to late pregnancy explained about 17% of late IL-6 concentrations (F (1,45) = 10.13, p = .003; adjusted R2 of 0.17).

Table 4.

Predictors of Late-Pregnancy IL-6 Concentrations (Models 2, 3, and 4).

Study Variable B SE(B) β t p-Value
Model 2*
Early E-DII1 .44 .13 .44 3.53 .001
Early BMI2 .06 .02 .29 2.26 .03
 R2 = .30; F (2,44) = 10.78, p < 0.001
Model 3^
Late BMI3 .08 .03 .35 2.56 .02
R2 = .11; F (1,46) = 6.56, p = .02
Model 4#
Delta E-DII4 -.51 .16 -.42 -3.18 .003
R2 = .17; F (1,45) = 10.13, p = .003
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Note. Model 2 describes early-pregnancy factors and late-pregnancy IL-6. Model 3 describes late-pregnancy factors and late-pregnancy IL-6. Model 4 describes change-over-pregnancy factors with late-pregnancy IL-6.

*Early-pregnancy physical activity was not included because it was non-significant and was removed from the model.

^Late-pregnancy physical activity and DII were not included because they were non-significant and were removed from the model.

#Change-over-pregnancy physical activity and BMI were not included because they were non-significant and were removed from the model.

1Early-pregnancy energy-adjusted Dietary Inflammatory Index score.

2Early-pregnancy BMI.

3Late-pregnancy BMI.

4Change-over-pregnancy energy-adjusted Dietary Inflammatory Index.

Discussion

Our results show that early-pregnancy diet and BMI explain the most variance of both early and late pregnancy IL-6 concentrations. The association of early-pregnancy E-DII and BMI with early- and late-pregnancy IL-6 concentrations accounted for at least 30% of the variance in both analyses, suggesting that early pregnancy factors may have a lasting influence on inflammatory profiles. Late-pregnancy BMI and change in E-DII diet scores were only marginal predictors of late-pregnancy IL-6.

The association between E-DII scores and IL-6 observed in our study supports earlier findings reporting that higher inflammation levels were associated with higher E-DII scores in pregnant women (Sen et al., 2016). While further studies are needed to investigate dietary effects on IL-6 in pregnancy, our results suggest a possible persistent relationship between early-pregnancy diet and IL-6 levels throughout pregnancy. If future studies support a persistent relationship between diet and IL-6, early pregnancy interventions or possibly interventions prior to pregnancy may be effective for altering pregnancy outcomes related to aberrant inflammation. The potential use of dietary interventions prior to pregnancy to reduce adverse pregnancy outcomes is supported by the work of Schoenaker and colleagues (2015) who reported in the Australian Longitudinal Study on Women’s Health cohort an inverse relationship between a Mediterranean diet prior to pregnancy and gestational hypertension. Nutritional interventions prior to pregnancy, possibly in conjunction with physical activity and weight loss, may be successful in reducing adverse pregnancy outcomes such as preeclampsia and gestational diabetes. Future studies should consider assessing biological measures such as inflammatory markers, metabolic profiles, or methylation changes to ascertain how behavioral interventions influence risk for developing adverse pregnancy outcomes and how interventions alter the maternal immunological response.

While both early- and late-pregnancy BMI were predictive of late IL-6 concentrations, early-pregnancy BMI had a stronger relationship with late IL-6 concentrations than did late-pregnancy BMI. The weaker relationship observed in late pregnancy may be related to the fact that we expect to see an elevated BMI as pregnancy progresses due to the growing placenta and fetus, and individuals who are overweight and obese are encouraged to gain 15–25 and 11–20 pounds, respectively, during pregnancy (American College of Obstetricians and Gynecologists, 2013). Berggren and colleagues (2016) have previously shown that overweight and obese individuals who gain within the Institute of Medicine guidelines (Institute of Medicine, 2009) have no further accumulation of body fat during pregnancy. In contrast, those who gain greater than IOM guidelines-recommended weight have further increases in fat mass accrual. The adipose tissue mass of our participants did not increase in late vs. early pregnancy (Table 2), suggesting that the increase in BMI was related to non-adipose tissues and weight gain in water (intravascular, extravascular, and amniotic fluid).

Early pregnancy BMI more closely reflects the prepregnancy condition where we would expect to observed elevated IL-6 levels in obese women. Late pregnancy BMI is elevated due to expected pregnancy weight gain, which does not involve increases in white adipose tissue that would result in higher IL-6 levels. However, the amount of adipose tissue present in early pregnancy is still present in late pregnancy, contributing to a relationship between late pregnancy IL-6 and late pregnancy BMI. Of note, our findings were the same whether we used BMI or measures of fat mass in the analysis. Overall, our data suggest that early-pregnancy obesity may be more influential in affecting inflammatory status than inflammation aggravating factors in late pregnancy, but these findings warrant further investigation to determine if early-pregnancy obesity indeed has more of an effect than late-pregnancy factors.

It is important to note that the cytokine profile, including that of IL-6, varies over the course of pregnancy, which could not be controlled for in our analysis. Ferguson and colleagues have reported that IL-6 is elevated in early pregnancy, declines slightly in mid-pregnancy, and is elevated again in late pregnancy (Ferguson et al., 2014). Additionally, previous studies have reported varying IL-6 trajectories in pregnancy related to prepregnancy BMI. Lean women appear to have higher IL-6 levels in late compared to early pregnancy than obese women, and obese women may have a more attenuated increase in IL-6 from early to late pregnancy (Pendeloski et al., 2017). The lack of significant change in IL-6 from early to late pregnancy in our sample then was expected since our cohort study was comprised entirely of overweight and obese women. Additional longitudinal studies, with more frequent measurements of multiple inflammatory markers and health behaviors, are required to confirm our findings in both lean and obese women.

Our analysis has several strengths. First, this is the first study, to our knowledge, to simultaneously examine the relationships between diet, physical activity, and BMI, and IL-6 concentrations throughout pregnancy. Second, the E-DII scores accounted for the inflammatory nature of subjects’ diets, allowing a more precise investigation of the relationship between inflammation and diet. Third, using the E-DII allows results from this study to be compared to other studies using the DII or E-DII. Fourth, the primary study was longitudinal and included early- and late-pregnancy time points. The late-pregnancy measures (34–36 weeks gestation) are later in gestation compared with many published data; thus, offering the opportunity to examine these relationships closer to the end of pregnancy.

Our analysis also had some limitations. First, the small sample size limits the generalizability of our findings and prohibits conducting separate stratified analyses in the intervention and control groups. However, given the strength of the relationships, we expect these results would be replicated in a larger study with an increased sample size. In a post-hoc power analysis, we demonstrated a power greater than .95. Secondly, at baseline, our participants did not meet recommended physical activity levels. Thus, our participants may not have maintained a high enough physical activity level to achieve the anti-inflammatory effects associated with physical activity prior to or during pregnancy, negating the ability to see a relationship between physical activity and IL-6 in this sample. Low physical activity levels may be a significant population risk factor, as 60% of reproductive-age women are either overweight or obese (Deputy et al., 2015). Finally, we examined only one inflammatory marker (IL-6) in this analysis.

Conclusion

These data provide evidence that early pregnancy dietary and obesity factors may exert a lasting influence on inflammatory markers in pregnancy. Our findings provide a strong basis for the development and testing of nurse-led health behavior interventions to reduce adverse pregnancy outcomes. Nutritional interventions, in conjunction with weight loss prior to pregnancy, may prove helpful in reducing inflammation-related pregnancy complications such as preeclampsia, gestational diabetes, and preterm birth. Nurse scientists are well-poised to develop, test, and deliver health behavior interventions. Nurses are also frequently charged with delivering health promotion education to patients in the clinical setting. Nurses are well-suited to continue this line of inquiry investigating the intersection of health behaviors and inflammation to prevent adverse pregnancy outcomes.

Footnotes

Authors' Note: This work was completed at Case Western Reserve University, MetroHealth, University of South Carolina.

Author Contributions: Wallace, M.K. contributed to conception and design, contributed to analysis and interpretation, drafted manuscript, critically revised manuscript, gave final approval, and agrees to be accountable for all aspects of work ensuring integrity and accuracy. Shivappa, N. contributed to analysis and interpretation, drafted manuscript, critically revised manuscript, gave final approval, and agrees to be accountable for all aspects of work ensuring integrity and accuracy. Wirth, M.D. contributed to analysis and interpretation, critically revised manuscript, gave final approval, and agrees to be accountable for all aspects of work ensuring integrity and accuracy. Hébert, J. R. contributed to analysis and interpretation, critically revised manuscript, gave final approval, and agrees to be accountable for all aspects of work ensuring integrity and accuracy. Huston-Gordesky, L. contributed to acquisition and interpretation, critically revised manuscript, gave final approval, and agrees to be accountable for all aspects of work ensuring integrity and accuracy. Alvarado, F. contributed to interpretation, critically revised manuscript, gave final approval, and agrees to be accountable for all aspects of work ensuring integrity and accuracy. Hauguel-de Mouzon, S. contributed to acquisition and interpretation, critically revised manuscript, gave final approval, and agrees to be accountable for all aspects of work ensuring integrity and accuracy. Catalano, P.M. contributed to conception and design, contributed to acquisition and interpretation, drafted manuscript, critically revised manuscript, gave final approval, and agrees to be accountable for all aspects of work ensuring integrity and accuracy.

Declaration of Conflicting Interests: The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: JRH owns controlling interest in Connecting Health Innovations LLC (CHI), a company that has licensed the rights to his invention of the Dietary Inflammatory Index (DII®). NS and MDW are employees of CHI. All other authors have no potential conflicts of interest to report.

Funding: The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was made possible by the Clinical and Translational Science Collaborative of Cleveland, UL1TR000439, from the National Center for Advancing Translational Sciences (NCATS) component of the National Institutes of Health and NIH Roadmap for Medical Research, and by NIH/NICHD (R01HD057236), NIH/NINR (T32NR015433; T32NR009759), and NIH/NIDDKD (R44DK103377).

ORCID iD: McKenzie K. Wallace Inline graphic https://orcid.org/0000-0002-0654-7539

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