Skip to main content
Download PDF

Adiposity mediates the association between whole grain consumption, glucose homeostasis and insulin resistance: findings from the US NHANES

Lipids in Health and Disease volume 17, Article number: 219 (2018) Cite this article

Abstract

Background

Growing evidence suggests an inverse association between whole grain (WG) consumption and insulin resistance (IR) or inflammation. However, it is still unclear whether adiposity plays a role in this relationship.

We investigated whether the associations between WG intake with IR, glucose homeostasis and inflammation are mediated by adiposity in US adults.

Methods

The 2005–2010 National Health and Nutrition Examination Surveys participants were included. WG intake was assessed and markers of IR and glucose homeostasis, inflammation, general and central adiposity. Analysis of co-variance and mediation analysis were applied, while accounting for survey design.

Results

Overall 16,621 participants were included in this analysis (mean age = 47.1 years, 48.3% men). After adjustment for age, gender, and race, mean C-reactive protein (CRP), apolipoprotein B (apo-B), fasting blood glucose (FBG), insulin, homeostatic model assessment of IR (HOMA-IR) and β cell function (HOMA-β), hemoglobin A1c (HbA1c), and 2 h glucose after an oral glucose tolerance test decreased with increasing quarters of WG (all p < 0.001). Body mass index (BMI) had significant mediation effects on the associations between WG intake and CRP, apo-B, fasting glucose, insulin, HOMA-IR, HOMA-B, HbA1c, triglyceride to high density lipoprotein-cholesterol (TG:HDL-C) ratio and triglyceride-glucose (TyG) index (all p < 0.05) after adjustment for age, gender, race/ethnicity, educational status, smoking and level of physical activity. Both waist circumference (WC) and anthropometrically predicted visceral adipose tissue (apVAT) mediated the association between WG intakes with CRP, FBG, HbA1c, TG:HDL-C ratio and TyG index, i.e. WC and apVAT had indirect effect (all p < 0.05).

Conclusion

Our findings provide insights into the favourable impact of WG consumption on IR and inflammation, which may be affected by both central and visceral adiposity, i.e. the link between WG with IR and inflammation is more mediated in overweight/obese compared with lean individuals.

Background

Diet is an important lifestyle component involved in the development of chronic diseases such as cardiovascular (CV) disease (CVD) and diabetes mellitus (DM). Studies have suggested that a high consumption of whole-grain (WG) foods is associated with a lower risk of CVD [1, 2] and DM [3]. In general however, the underlying mechanisms of these associations have not been completely elucidated, but could involve adiposity. This has been supported by long-term data showing a reduction of postprandial insulin and glucose responses after 2–4 weeks of WG rye or wheat diet in overweight men [4, 5]. Further studies reported that WG intake reduced the risk of coronary heart disease (CHD), CVD and cancer, as well as all-cause death and mortality from respiratory diseases, infectious diseases, DM, and other non-CV and non-cancer causes [5]. However, randomized control trials (RCTs) found no significant effect of WG foods on major CVD risk factors [6,7,8]. In this context, a recent systematic review and meta-analysis of 9 RCTs indicated that there is insufficient evidence on the effect of WG diets on CV outcomes or major CVD risk factors [8].

Mediation analysis could clarify the role of adiposity, if any, in the relation between WG intake and cardiometabolic risk [9]. From a statistical standpoint, mediation analysis can be used to explore and quantify the extent to which the relationship between an exposure and an outcome of interest occurs through the effect of a third variable. The traditional approach to mediation analysis tends to produce a bias when there is a confounding uncontrolled mediator outcome or an interaction between exposure and mediator. Using the counterfactual framework in mediation analysis, unbiased valid estimates of direct and indirect effects can be obtained [9, 10].

The present study aimed to evaluate the associations between WG consumption and markers of glucose/insulin homeostasis and inflammation, as well as to assess the mediation effect of adiposity parameters on these relationships using “mediation analysis”. These analyses were applied to a large, representative sample of US adults.

Methods

Population characteristics

The National Health and Nutrition Examination Survey (NHANES) is a series of ongoing repeated cross-sectional surveys conducted by the US National Center for Health Statistics (NCHS) [11]. The NCHS Research Ethics Review Board approved the NHANES protocol and consent was obtained from all participants [12]. The current study was based on analysis of data collected from 2005 to 2010. Data collection on demographics occurs through in-home administered questionnaires, while anthropometric, inflammation and biochemistry data were collected by trained personnel using mobile exam centers. More detailed information is available elsewhere [13, 14]. Data on level of education were collected as self-report to this question “What is the highest grade or level of school completed or the highest degree received?”

Glycated hemoglobin (HbA1c) was measured using a Tosoh A1C 2.2 Plus Glycohemoglobin Analyzer (San Francisco, USA). Plasma glucose [fasting and 2 h post oral glucose tolerance test (OGTT)] was measured by a hexokinase method using a Roche/Hitachi 911 Analyzer and Roche Modular P Chemistry Analyzer (Organon Technika, Durham, NC). Other biological tests details are available in the NHANES Laboratory/Medical Technologists Procedures Manual [15]. C-reactive protein (CRP) levels were measured by latex-enhanced nephelometry [14]. Homeostatic model assessment of insulin resistance (HOMA-IR) and β-cell function (HOMA-B) were calculated as follows: HOMA-IR = [fasting glucose-FBG (nmol/L) * fasting insulin (mU/mL)/22.5], and HOMA-B = [20 × fasting insulin (μU/ml)]/ [FBG (mmol/l) − 3·5] [16]. Triglyceride-glucose (TyG) index was calculated as ln[fasting triglyceride;TG (mg/dl) × FBG (mg/dl)/2] [16]. The anthropometrically predicted visceral adipose tissue (apVAT) was calculated with gender-specific validated equations: for men: 6 *waist circumference (WC) – 4.41 * proximal thigh circumference + 1.19 * age – 213.65; for women: 2.15 * WC – 3.63 * proximal thigh + 1.46 * age + 6:22 * body mass index (BMI) -92.713 for women [17].

WG consumption

Dietary intake was assessed via 24 h recall obtained by a trained interviewer during the mobile examination center visit, with the use of a computer-assisted dietary interview system with standardized probes, i.e. the US Department of Agriculture Automated Multiple-Pass Method (AMPM) [18]. Briefly, the type and quantity of all foods and beverages consumed in a single 24 h period before the dietary interview (from midnight to midnight) were collected with the use of AMPM. AMPM is designed to enhance complete and accurate data collection while reducing respondent burden [18, 19]. Detailed descriptions of the dietary interview methods are provided in the NHANES Dietary Interviewer’s Training Manual [20]. The MyPyramid Equivalents Database for USDA Survey Food Codes was used to calculate WG intake [20].

Statistical analysis

Data analyses followed the Centers for Disease Control and Prevention (CDC) guidelines for analysis of complex NHANES data, accounted for the masked variance and used the recommended weighting methodology [21]. All analyses were conducted with the use of SPSS version 22.0 (IBM Corp, Armonk, NY). The Kolmogorov-Smirnov test was used to evaluate the normal distribution of variables. We computed age, race, and gender-adjusted mean of insulin resistance (IR) or inflammatory markers across the quarters of WG consumption, using analysis of covariance (ANCOVA). Multi-collinearity for the multiple linear regressions was assessed with variance inflation factors (VIF) at each step [22]. Multi-collinearity was considered high when the VIF was > 10 [22].

In the present study, we assessed the total, direct, and indirect effects of WG intake on IR or inflammation with BMI, WC or apVAT as a mediator by using the counterfactual framework [23, 24]. This method has been fully explained elsewhere [25, 26]. In this approach, total effect can be divided into direct effect (not mediated by BMI, WC or apVAT) and indirect effect (mediated by BMI, WC or apVAT). The SPSS Macro developed by Preacher and Hayes [26] was used to evaluate the direct and indirect effect of WG intake on IR or inflammation with BMI, WC or apVAT as mediators.

Results

General characteristics

A total of 16,621 participants met the criteria for inclusion in the current analysis. Their characteristics are summarized in Table 1. Overall, mean age was 47.1 years and 48.3% of the participants were men. Non-Hispanic white (69.4%) was the largest racial group and other Hispanic (4.5%) the smallest racial group. Furthermore, 56.1% of the participants were married, while 56.4% had achieved more than high school (Table 1). Mean and standard error of mean (SEM) of BMI, WC and apVAT were 28.7 ± 0.05 (kg/m2), 98 ± 0.1 (cm) and 179.2 ± 1.2, respectively (Table 1).

Table 1 Demographic and clinical characters of subjects
Full size table

Age, gender and race-adjusted mean of markers of IR and inflammation (i.e. CRP, apolipoprotein B (apo-B), FBG, insulin, HOMA-IR, HOMA-B, HbA1c, 2 h glucose and TG:high density lipoprotein-cholesterol (HDL-C) ratio were significantly decreased across increasing quarters of WG consumption (p < 0.001 for all comparisons). In contrast, there were no significant differences in TyG index across quarters of WG consumption (p = 0.082, Table 2).

Table 2 Age-, sex-, and race-adjusted mean of markers of insulin resistance and inflammation across quartiles of WG consumption
Full size table

Association between WG intake, BMI, WC, apVAT and markers of IR and inflammation

With regard to the action theory [i.e. the examination of the link between WG and hypothesized mediators (BMI, WC, apVAT), after multivariable adjustment], there was a significant association between BMI (β: − 0.255, p < 0.001), WC (− 0.446, p < 0.001), apVAT (− 1.620, p = 0.036) and WG intake (Table 2). When the “total effect” was calculated by examining the association between WG intake and markers of IR or inflammation in multivariable models without adjusting for potential mediators, with the exception of apo-B and HOMA-B, markers of IR or inflammation were negatively and significantly associated with WG intake (p < 0.001 for all comparisons, Table 3). When the ‘conceptual theory’ was tested [i.e. the association between mediators (BMI, WC and apVAT) and markers of IR or inflammation], all potential mediators had significant and positive associations with markers of IR or inflammation (p < 0.001 for all comparisons 1, Table 4).

Table 3 Estimates of regression coefficients (95% CIs) for the association between the whole grain intake, BMI, WC, apVAT (action theory), and markers of insulin resistance and inflammation (total effect) among US adults in NHANES
Full size table
Table 4 Estimates of regression coefficients (95% CIs) for the association between BMI, WC, apVAT and markers of insulin resistance and inflammation (conceptual theory) among US adults
Full size table

Direct and indirect effects of WG intake on markers of IR and inflammation with BMI, WC and apVAT as mediators

Table 5 shows the direct effect, indirect effect, proportion of mediation effect, and Sobel statistics for testing indirect effects. For BMI, the mediated effects (indirect effect) were significant for the associations between WG intake and CRP, apo-B, FBG, plasma insulin, HOMA-IR, HOMA-B, HbA1c, TG;HDL-C ratio and TyG index (p < 0.05 for all comparisons), but not for 2 h glucose. Furthermore, WC and apVAT mediated the association between WG intake and markers of IR and inflammation, including CRP, FBG, HbA1c and TyG index (p < 0.05 for all comparisons).

Table 5 Direct and indirect effects of whole grain consumption on markers of insulin resistance and inflammation with BMI, WC and apVAT as mediators among US adults
Full size table

Direct effects showed that WG was associated with CRP, FBG, plasma insulin, HOMA-IR, HbA1c and 2 h glucose, even after adjustment for BMI or WC. In contrast, for apVAT, the estimates of direct effects were not significant. The highest mediated effects by BMI, WC and apVAT were those for HOMA-B (84.3%), TyG index (52.1%) and CRP (62.3%), respectively.

Discussion

The mediation analysis conducted in the current study supports a relationship between WG intake and markers of IR and inflammation, being, at least partially, mediated by the effect of both general and central adiposity (Fig. 1). Altogether, our findings suggest some pathways through which WG consumption may affect the long-term risk of chronic diseases, including CVD and DM.

Fig. 1
figure 1

Mediation model for the association between whole grain (WG) intake, insulin resistance (IR) and inflammation with body mass index (BMI), waist circumference (WC) and anthropometrically-predicted visceral adipose tissue (apVAT) as mediators. Path α represents the regression coefficient for the association of WG intake with BMI, WC and apVAT. Path β represents the regression coefficient for the association of BMI, WC and apVAT with IR and inflammation. The product of regression coefficients α and β represents the mediated effect (indirect effect) of BMI, WC or apVAT (α#β). Path £’ represents the direct effect of WG intake with IR and inflammation, after adjustment for BMI, WC or apVAT. Path £ represents the simple total effect of WG intake on IR and inflammation, without adjustment for BMI, WC or apVAT

Full size image

Our findings are in line with previous observational and interventional studies that demonstrated an inverse association between WG consumption and inflammatory markers [27,28,29]. In contrast, there are also studies showing no effects of WG on CRP or interleukin (IL)-6 [7, 30]; low statistical power may, at least in part, explain these discripancies. Several mechanisms have been proposed in relation to the health benefits induced by WG. It has been reported that WG are a rich source of fiber, minerals [magnesium (Mg), potassium, phosphorous, selenium, manganese, zinc, and iron], vitamins (especially high in vitamins B and E), phenolic compounds, phytoestrogens (lignans) and related antioxidants [31]. These compounds have been found to favorably affect markers of inflammation and glucose homeostasis [32, 33]. Dietary fibers in WG can also exert their anti-inflammatory action by decreasing lipid oxidation, which in turn is associated with reduced inflammation [34].

In line with our findings, in two reports from the Framingham Offspring Cohort Study, the number of daily servings of WG foods was inversely related with fasting insulin concentrations and IR, but not HbA1c [35, 36]. In the Insulin Resistance Atherosclerosis Study, a greater intake of dark bread and cereals were associated with higher insulin sensitivity [37]. A similar association between WG intake and insulin sensitivity was observed in a cross-sectional study of 285 adolescents from Minnesota [38]. In these studies, the associations were more pronounced in overweight participants [36, 38].

In contrast, a smaller study carried out in Finland did not find a significant difference in incident type 2 DM (T2DM) when comparing consumers of WG foods above and below the extreme quartiles of intake, although there was a trend suggesting a reduced risk of developing T2DM among greater consumers of WG foods [39]. Liu et al. found that the risk of T2DM increased across quintiles of refined: WG food intake ratio. In detail, after adjustment for age and energy intake, women in the highest quintile of refined WG: food intake had a 57% greater risk of incident T2DM compared with women in the lowest intake ratio [40]. Pereira et al. compared insulin sensitivity in overweight T2DM patients on diets that incorporated refined or WG; a greater rate of glucose infusion was achieved during an euglycemic hyperinsulinemic clamp test in those patients on WG diet [41].

With regard to the beneficial impact of WG on glucose and insulin metabolism, a number of possible mechanisms have been proposed. WG foods are known to delay digestion and absorption of carbohydrates. Food structure has been found to be more important than gelatinization or the presence of viscous dietary fiber in determining glycemic response [42]. Refining grains tends to increase the glycemic response, whereas WG tend to slow it [43]. Other properties of WG, which make them beneficial for health and glucose/insulin balance, could be their relatively high antioxidant activity [44]. Soluble antioxidants include phenolic acids, flavonoids, tocopherols and avenanthramides in oats [44]. Studies suggested that T2DM may be reduced by dietary antioxidants intake [39, 45]. Grain lipids comprise about 75 g unsaturated fatty acids/100 g, of which there are approximately equal amounts of oleic and linoleic acid and 1–2 g linolenic acid/100 g. Therefore, WG consist of a high load of unsaturated fatty acids which can reduce the risk of T2DM [46]. Furthermore, WG also contain phytic acid, lectins, phenolics, amylase inhibitors and saponins which have also been shown to lower plasma glucose and insulin [47].

It has been reported that dietary fiber could be affected by macrobiota (fermentation) in the colon [48] and produce short chain fatty acids (SCFA), including acetate, butyrate and propionate [49]. Several mechanisms linking SCFA to insulin sensitivity and T2DM have been proposed, including the inter-organ effects on adipose tissue function and lipid storage capacity, inflammatory profile, as well as liver and skeletal muscle substrate metabolism [50,51,52].

Another finding of the present analysis was that adiposity parameters could, at least partially, mediate the associations between WG and IR or inflammatory markers. Observational studies have also shown an inverse relationship between WG intake and BMI, WC and risk of weight gain [53]. In particular, central adiposity, has been strongly associated with plasma inflammatory protein concentrations, and visceral adipose tissue is known to secrete a number of pro-inflammatory adipokines, including tumor necrosis factor a, IL-6 and Plasminogen activator inhibitor-1 (PAI-1) [53]. Therefore, it is possible that WG intake could be related to lower inflammatory protein levels by preventing weight increase, promoting weight maintenance, and reducing visceral adiposity. In another study, the authors applied the same techniques on cross-sectional data from 4700 adults aged 20–90 years, and reported that reducing abdominal obesity might play an important role in the pathway through which Mediterranean diet consumption reduces insulin resistance and inflammation [54]. With regard to the role of adiposity on the association between CRP and WG consumption, the 4 studies that evaluated the strength of the association between dietary patterns and CRP before and after adjustments for indices of adiposity, have shown a weakening of the association for 3 studies [55,56,57] and a strengthening for 1 [58]. A weakening association following the adjustment could be consistent with at least part of the effects of the dietary pattern being mediated through changes in adiposity and/or adipose tissue metabolism. In the present study, we found that link between WG with IR and inflammation is more mediated in overweight/obese individuals compared with lean. These findings support the implementation of WG intake in both overweight-obese individuals (who usually also have increased IR and inflammation) and normal-weight individuals to minimize the development/progression of these metabolic disorders.

The current study has major strengths, including the use of cause mediation to investigate the effects of general and central adiposity on the relationship of WG intake with IR and inflammation. The large sample size afforded adequate statistical power to conduct the complex analysis with little risk of multiple comparisons effects. The quality of metabolic and anthropometric measurements obtained at the NHANES study visit, including WC measurement as a marker of visceral adiposity and a glucose tolerance test as a measure of insulin sensitivity, allowed for the adjustment of these variables with a high degree of precision, potentially providing insight to the pathway by which WG consumption could influence inflammatory markers and insulin/glucose homeostasis.

Our analysis also has limitations. The cross-sectional nature precludes any reliable establishment of the sequence of events between WG intake, change in adiposity and IR/inflammation. It is however, less an expectation that IR or inflammation can cause individuals not to consume WG, while the reciprocal relationship between adiposity and IR/inflammation has been largely documented. The mediated effect of WC may be affected by BMI, or vice versa, because of the high correlation between WC and BMI. In the usual databases, this could have been addressed by considering 2 mediators (BMI and WC) simultaneously in a model [45]. However, this was not feasible in the conventional or causal mediation models with the use of the complex survey design as in the present study. To address this point, we used other adiposity markers, i.e. apVAT. Although BMI and WC are commonly used to estimate obesity, these markers may be inaccurate and can lead to bias in measuring adiposity. Therefore, the association between WG consumption and overall adiposity can be underestimated when BMI is used as a marker of adiposity. Moreover, because of a high chance of co-linearity, we excluded potential confounding factors, which could have increased the chance of residual bias in our models. Lastly, we need to mention that, due to the nature of our study (cross-sectional), reverse causality for the determination of the cause and effect (direction of causality) could be present. In this regard, RCTs are warranted to confirm our results.

The current study has significant clinical and public health implications, regarding the role of adiposity in the association between WG consumption, IR and inflammation markers. This is a necessary and important step toward public health policy making and raising public awareness. Our study provides a comprehensive snapshot of dietary correlates of IR and inflammation markers at a national level and, given the nature of this study, it could be extrapolated to the US adult population.

Conclusions

In conclusion, our findings, based on a large and representative sample size, provide insights into the protective role of WG intake on IR and inflammatory markers, which may be mediated, at least partially, by adiposity. This finding may have implications on health policies in relation to dietary strategies to prevent and control obesity-related conditions involving IR or systemic inflammation.

Abbreviations

apo-B:

Apolipoprotein B

BMI:

Body mass index

CHD:

Coronary heart disease

CRP:

C-reactive protein

CVD:

Cardiovascular disease

DM:

Diabetes mellitus

FBG:

Fasting blood glucose

HbA1c :

Hemoglobin A1c

HDL-C:

High density lipoprotein-cholesterol

HOMA-IR:

Homeostatic model assessment of IR

HOMA-β:

β cell function

IR:

Insulin resistance

NCHS:

National Center for Health Statistics

NHANES:

National Health and Nutrition Examination Survey

RCT:

Randomized control trials

TyG:

Triglyceride-glucose

WG:

Whole grain

References

  1. Jacobs DR Jr, Meyer KA, Kushi LH, Folsom AR. Whole-grain intake may reduce the risk of ischemic heart disease death in postmenopausal women: the Iowa Women's health study. Am J Clin Nutr. 1998;68(2):248–57.

    Article  CAS  Google Scholar 

  2. Liu S, Stampfer MJ, Hu FB, Giovannucci E, Rimm E, Manson JE, et al. Whole-grain consumption and risk of coronary heart disease: results from the Nurses’ health study. Am J Clin Nutr. 1999;70(3):412–9.

    Article  CAS  Google Scholar 

  3. Salmeron J, Manson JE, Stampfer MJ, Colditz GA, Wing AL, Willett WC. Dietary fiber, glycemic load, and risk of non-insulin-dependent diabetes mellitus in women. JAMA. 1997;277(6):472–7.

    Article  CAS  Google Scholar 

  4. McIntosh GH, Noakes M, Royle PJ, Foster PR. Whole-grain rye and wheat foods and markers of bowel health in overweight middle-aged men. Am J Clin Nutr. 2003;77(4):967–74.

    Article  CAS  Google Scholar 

  5. Aune D, Keum N, Giovannucci E, Fadnes LT, Boffetta P, Greenwood DC, et al. Whole grain consumption and risk of cardiovascular disease, cancer, and all cause and cause specific mortality: systematic review and dose-response meta-analysis of prospective studies. BMJ (Clin Res Ed). 2016;353:i2716.

    Google Scholar 

  6. Tighe P, Duthie G, Vaughan N, Brittenden J, Simpson WG, Duthie S, et al. Effect of increased consumption of whole-grain foods on blood pressure and other cardiovascular risk markers in healthy middle-aged persons: a randomized controlled trial. Am J Clin Nutr. 2010;92(4):733–40.

    Article  CAS  Google Scholar 

  7. Brownlee IA, Moore C, Chatfield M, Richardson DP, Ashby P, Kuznesof SA, et al. Markers of cardiovascular risk are not changed by increased whole-grain intake: the WHOLEheart study, a randomised, controlled dietary intervention. Br J Nutr. 2010;104(1):125–34.

    Article  CAS  Google Scholar 

  8. Kelly SA, Hartley L, Loveman E, Colquitt JL, Jones HM, Al-Khudairy L, et al. Whole grain cereals for the primary or secondary prevention of cardiovascular disease. Cochrane Database Syst Rev. 2017;8:Cd005051.

    PubMed  Google Scholar 

  9. Richiardi L, Bellocco R, Zugna D. Mediation analysis in epidemiology: methods, interpretation and bias. Int J Epidemiol. 2013;42(5):1511–9.

    Article  Google Scholar 

  10. Robins JM, Greenland S. Identifiability and exchangeability for direct and indirect effects. Epidemiology. 1992;3(2):143–55.

    Article  CAS  Google Scholar 

  11. Mazidi M, Shivappa N, Wirth MD, Hebert JR, Kengne AP. Greater Dietary Inflammatory Index score is associated with higher likelihood of chronic kidney disease. Br J Nutr. 2018;120(2):204–09. https://doi.org/10.1017/S0007114518001071.

    Article  CAS  PubMed  Google Scholar 

  12. Mazidi M, Kengne AP, Vatanparast H. Association of dietary patterns of American adults with bone mineral density and fracture. Public Health Nutr. 2018;21:1–7. https://doi.org/10.1017/S1368980018000939. [Epub ahead of print].

    Article  Google Scholar 

  13. Kalk WJ, Joffe BI. The metabolic syndrome, insulin resistance, and its surrogates in African and white subjects with type 2 diabetes in South Africa. Metab Syndr Relat Disord. 2008;6(4):247–55.

    Article  CAS  Google Scholar 

  14. Mazidi M, Mikhailidis DP, Banach M. Higher dietary acid load is associated with higher likelihood of peripheral arterial disease among American adults. J Diabetes Complications. 2018;32(6):565-569. https://doi.org/10.1016/j.jdiacomp.2018.03.001. Epub 2018 Mar 9.

    Article  Google Scholar 

  15. National Center for Health Statistics CfDCaPNHaNESA, http://www.cdc.gov/nchs/nhanes.htmf.

  16. Mazidi M, Katsiki N, Mikhailidis DP, Banach M. The link between insulin resistance parameters and serum uric acid is mediated by adiposity. Atherosclerosis. 2018;270:180–86. https://doi.org/10.1016/j.atherosclerosis.2017.12.033. Epub 2017 Dec 26.

    Article  CAS  PubMed  Google Scholar 

  17. Samouda H, Dutour A, Chaumoitre K, Panuel M, Dutour O, Dadoun F. VAT=TAAT-SAAT: innovative anthropometric model to predict visceral adipose tissue without resort to CT-scan or DXA. Obesity (Silver Spring). 2013;21(1):E41–50.

    Article  Google Scholar 

  18. Ahluwalia N, Dwyer J, Terry A, Moshfegh A, Johnson C. Update on NHANES dietary data: focus on collection, release, analytical considerations, and uses to inform public policy. Adv Nutr. 2016;7(1):121–34.

    Article  Google Scholar 

  19. Moshfegh AJ, Rhodes DG, Baer DJ, Murayi T, Clemens JC, Rumpler WV, et al. The US Department of Agriculture Automated Multiple-Pass Method reduces bias in the collection of energy intakes. Am J Clin Nutr. 2008;88(2):324–32.

    Article  CAS  Google Scholar 

  20. Bowman SAF, J.E.; Moshfegh, A. MyPyramid Equivalents Database, 2.0 for USDA Survey Foods; Food Surveys Research Group, Beltsville Human Nutr Res Center, Agricultural Research Service, U.S. Department of Agriculture: Beltsville, MD, USA.Available online: http://www.ars.usda.gov/ba/bhnrc/fsrg (accessed on 10 Apr 2017); 2008.

  21. Mazidi M, Gao HK, Kengne AP. Lipid accumulation product and visceral adiposity index are associated with dietary patterns in adult Americans. Medicine (Baltimore). 2018;97(19):e0322. https://doi.org/10.1097/MD.0000000000010322.

    Article  CAS  Google Scholar 

  22. Slinker BK, Glantz SA. Multiple regression for physiological data analysis: the problem of multicollinearity. Am J Phys. 1985;249(1 Pt 2):R1–12.

    CAS  Google Scholar 

  23. VanderWeele TJ. Mediation and mechanism. Eur J Epidemiol. 2009;24(5):217–24.

    Article  Google Scholar 

  24. VanderWeele TJ. A three-way decomposition of a total effect into direct, indirect, and interactive effects. Epidemiology. 2013;24(2):224–32.

    Article  Google Scholar 

  25. Mazidi M, Katsiki N, Mikhailidis DP, Banach M. The link between insulin resistance parameters and serum uric acid is mediated by adiposity. Atherosclerosis. 2017;270:180–6.

    Article  Google Scholar 

  26. Preacher KJ, Hayes AF. Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behav Res Methods. 2008;40(3):879–91.

    Article  Google Scholar 

  27. Qi L, van Dam RM, Liu S, Franz M, Mantzoros C, Hu FB. Whole-grain, bran, and cereal fiber intakes and markers of systemic inflammation in diabetic women. Diabetes Care. 2006;29(2):207–11.

    Article  CAS  Google Scholar 

  28. Jensen MK, Koh-Banerjee P, Franz M, Sampson L, Gronbaek M, Rimm EB. Whole grains, bran, and germ in relation to homocysteine and markers of glycemic control, lipids, and inflammation 1. Am J Clin Nutr. 2006;83(2):275–83.

    Article  CAS  Google Scholar 

  29. Lutsey PL, Jacobs DR Jr, Kori S, Mayer-Davis E, Shea S, Steffen LM, et al. Whole grain intake and its cross-sectional association with obesity, insulin resistance, inflammation, diabetes and subclinical CVD: the MESA study. Br J Nutr. 2007;98(2):397–405.

    Article  CAS  Google Scholar 

  30. Fukagawa NK, Anderson JW, Hageman G, Young VR, Minaker KL. High-carbohydrate, high-fiber diets increase peripheral insulin sensitivity in healthy young and old adults. Am J Clin Nutr. 1990;52(3):524–8.

    Article  CAS  Google Scholar 

  31. Marquart L, Slavin JL, Fulcher TG, eds. Whole-grain foods in health and disease. St Paul, MN: American Association of Cereal Chemists, Inc,2002.

  32. Ajani UA, Ford ES, Mokdad AH. Dietary fiber and C-reactive protein: findings from national health and nutrition examination survey data. J Nutr. 2004;134(5):1181–5.

    Article  CAS  Google Scholar 

  33. King DE, Egan BM, Geesey ME. Relation of dietary fat and fiber to elevation of C-reactive protein. Am J Cardiol. 2003;92(11):1335–9.

    Article  CAS  Google Scholar 

  34. Duntas LH. Selenium and inflammation: underlying anti-inflammatory mechanisms. Horm Metab Res. 2009;41(6):443–7.

    Article  CAS  Google Scholar 

  35. McKeown NM, Meigs JB, Liu S, Saltzman E, Wilson PW, Jacques PF. Carbohydrate nutrition, insulin resistance, and the prevalence of the metabolic syndrome in the Framingham offspring cohort. Diabetes Care. 2004;27(2):538–46.

    Article  Google Scholar 

  36. McKeown NM, Meigs JB, Liu S, Wilson PW, Jacques PF. Whole-grain intake is favorably associated with metabolic risk factors for type 2 diabetes and cardiovascular disease in the Framingham offspring study. Am J Clin Nutr. 2002;76(2):390–8.

    Article  CAS  Google Scholar 

  37. Liese AD, Roach AK, Sparks KC, Marquart L, D'Agostino RB, Jr., Mayer-Davis EJ. Whole-grain intake and insulin sensitivity: the insulin resistance atherosclerosis study. Am J Clin Nutr 2003;78(5): 965–971.

    Article  CAS  Google Scholar 

  38. Steffen LM, Jacobs DR Jr, Murtaugh MA, Moran A, Steinberger J, Hong CP, et al. Whole grain intake is associated with lower body mass and greater insulin sensitivity among adolescents. Am J Epidemiol. 2003;158(3):243–50.

    Article  Google Scholar 

  39. Montonen J, Knekt P, Jarvinen R, Aromaa A, Reunanen A. Whole-grain and fiber intake and the incidence of type 2 diabetes. Am J Clin Nutr. 2003;77(3):622–9.

    Article  CAS  Google Scholar 

  40. Liu S, Manson JE, Stampfer MJ, Hu FB, Giovannucci E, Colditz GA, et al. A prospective study of whole-grain intake and risk of type 2 diabetes mellitus in US women. Am J Public Health. 2000;90(9):1409–15.

    Article  CAS  Google Scholar 

  41. Jang Y, Lee JH, Kim OY, Park HY, Lee SY. Consumption of whole grain and legume powder reduces insulin demand, lipid peroxidation, and plasma homocysteine concentrations in patients with coronary artery disease: randomized controlled clinical trial. Arterioscler Thromb Vasc Biol. 2001;21(12):2065–71.

    Article  CAS  Google Scholar 

  42. Granfeldt Y, Hagander B, Bjorck I. Metabolic responses to starch in oat and wheat products. On the importance of food structure, incomplete gelatinization or presence of viscous dietary fibre. Eur J Clin Nutr. 1995;49(3):189–99.

    CAS  PubMed  Google Scholar 

  43. Jenkins DJ, Wolever TM, Jenkins AL, Giordano C, Giudici S, Thompson LU, et al. Low glycemic response to traditionally processed wheat and rye products: bulgur and pumpernickel bread. Am J Clin Nutr. 1986;43(4):516–20.

    Article  CAS  Google Scholar 

  44. Slavin J. Why whole grains are protective: biological mechanisms. Proc Nutr Soc. 2003;62(1):129–34.

    Article  CAS  Google Scholar 

  45. Montonen J, Knekt P, Jarvinen R, Reunanen A. Dietary antioxidant intake and risk of type 2 diabetes. Diabetes Care. 2004;27(2):362–6.

    Article  CAS  Google Scholar 

  46. Madigan C, Ryan M, Owens D, Collins P, Tomkin GH. Dietary unsaturated fatty acids in type 2 diabetes: higher levels of postprandial lipoprotein on a linoleic acid-rich sunflower oil diet compared with an oleic acid-rich olive oil diet. Diabetes Care. 2000;23(10):1472–7.

    Article  CAS  Google Scholar 

  47. Slavin JL, Martini MC, Jacobs DR Jr, Marquart L. Plausible mechanisms for the protectiveness of whole grains. Am J Clin Nutr. 1999;70(3 Suppl):459s–63s.

    Article  CAS  Google Scholar 

  48. Mazidi M, Rezaie P, Kengne AP, Mobarhan MG, Ferns GA. Gut microbiome and metabolic syndrome. Diabetes Metab Syndr. 2016;10(2 Suppl 1):S150–7.

    Article  Google Scholar 

  49. Topping DL, Clifton PM. Short-chain fatty acids and human colonic function: roles of resistant starch and nonstarch polysaccharides. Physiol Rev. 2001;81(3):1031–64.

    Article  CAS  Google Scholar 

  50. De Vadder F, Kovatcheva-Datchary P, Goncalves D, Vinera J, Zitoun C, Duchampt A, et al. Microbiota-generated metabolites promote metabolic benefits via gut-brain neural circuits. Cell. 2014;156(1–2):84–96.

    Article  CAS  Google Scholar 

  51. den Besten G, Bleeker A, Gerding A, van Eunen K, Havinga R, van Dijk TH, et al. Short-chain fatty acids protect against high-fat diet-induced obesity via a PPARgamma-dependent switch from lipogenesis to fat oxidation. Diabetes. 2015;64(7):2398–408.

    Article  CAS  Google Scholar 

  52. Murphy EF, Cotter PD, Healy S, Marques TM, O'Sullivan O, Fouhy F, et al. Composition and energy harvesting capacity of the gut microbiota: relationship to diet, obesity and time in mouse models. Gut. 2010;59(12):1635–42.

    Article  CAS  Google Scholar 

  53. Williams PG, Grafenauer SJ, O'Shea JE. Cereal grains, legumes, and weight management: a comprehensive review of the scientific evidence. Nutr Rev. 2008;66(4):171–82.

    Article  Google Scholar 

  54. Park YM, Zhang J, Steck SE, Fung TT, Hazlett LJ, Han K, et al. Obesity mediates the association between Mediterranean diet consumption and insulin resistance and inflammation in US adults. J Nutr. 2017;147(4):563–71.

    Article  CAS  Google Scholar 

  55. Esmaillzadeh A, Kimiagar M, Mehrabi Y, Azadbakht L, Hu FB, Willett WC. Dietary patterns and markers of systemic inflammation among Iranian women. J Nutr. 2007;137(4):992–8.

    Article  CAS  Google Scholar 

  56. Nettleton JA, Schulze MB, Jiang R, Jenny NS, Burke GL, Jacobs DR. A priori–defined dietary patterns and markers of cardiovascular disease risk in the multi-ethnic study of atherosclerosis (MESA). Am J Clin Nutr. 2008;88(1):185–94.

    Article  CAS  Google Scholar 

  57. Nettleton JA, Steffen LM, Mayer-Davis EJ, Jenny NS, Jiang R, Herrington DM, et al. Dietary patterns are associated with biochemical markers of inflammation and endothelial activation in the multi-ethnic study of atherosclerosis (MESA). Am J Clin Nutr. 2006;83(6):1369–79.

    Article  CAS  Google Scholar 

  58. Lopez-Garcia E, Schulze MB, Fung TT, Meigs JB, Rifai N, Manson JE, et al. Major dietary patterns are related to plasma concentrations of markers of inflammation and endothelial dysfunction. Am J Clin Nutr. 2004;80(4):1029–35.

    Article  CAS  Google Scholar 

Download references

Availability of data and materials

The datasets analyzed during the current study are publically available in the National Health and Nutrition Examination Survey (NHANES), [https://wwwn.cdc.gov/Nchs/Nhanes].

Author information

Authors and Affiliations

  1. Key State Laboratory of Molecular Developmental Biology,Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, 100101, People’s Republic of China

    Mohsen Mazidi

  2. Second Propedeutic Department of Internal Medicine, Medical School, Aristotle University of Thessaloniki, Hippokration Hospital, Thessaloniki, Greece

    Niki Katsiki

  3. Non-Communicable Disease Research Unit, South African Medical Research Council and University of Cape Town, Cape Town, South Africa

    Andre Pascal Kengne

  4. Department of Clinical Biochemistry, Royal Free Campus, University College London Medical School, University College London (UCL), London, UK

    Dimitri P. Mikhailidis

  5. Department of Hypertension, Chair of Nephrology and Hypertension, Medical University of Lodz, Lodz, Poland

    Maciej Banach

  6. Polish Mother’s Memorial Hospital Research Institute (PMMHRI), Lodz, Poland

    Maciej Banach

  7. Cardiovascular Research Centre, University of Zielona Gora, Zielona Gora, Poland

    Maciej Banach

Authors
  1. Mohsen Mazidi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Niki Katsiki
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Andre Pascal Kengne
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Dimitri P. Mikhailidis
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Maciej Banach
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

MM: Study conception and design, Analysis and interpretation of data. Drafting of manuscript. NK, APK, DPM, MB: Study conception and design, Critical revision. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Mohsen Mazidi.

Ethics declarations

Competing interest

NK has given talks, attended conferences and participated in trials sponsored by Amgen, Angelini, Astra Zeneca, Boehringer Ingelheim, Galenica, MSD, Novartis, Novo Nordisk, Sanofi and Win Medica. DPM has given talks and attended conferences sponsored by MSD, AstraZeneca and Libytec. The rest of the authors have declare that they have no competing interests.

Ethics approval and consent to participate

Informal consent was obtained from all adult participants of the NHANES and the protocol was approved by the NCHS Research Ethics Review Board.

Consent for publication

All the authors read and approved the last version of the manuscript.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mazidi, M., Katsiki, N., Kengne, A.P. et al. Adiposity mediates the association between whole grain consumption, glucose homeostasis and insulin resistance: findings from the US NHANES. Lipids Health Dis 17, 219 (2018). https://doi.org/10.1186/s12944-018-0805-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12944-018-0805-6

Keywords

Lipids in Health and Disease

ISSN: 1476-511X