Postprandial Responses to a Standardised Meal in Hypertension: The Mediatory Role of Visceral Fat Mass

Panayiotis Louca, Sarah E Berry, Kate Bermingham, Paul W Franks, Jonathan Wolf, Tim D Spector, Ana M Valdes, Phil Chowienczyk, Cristina Menni, Panayiotis Louca, Sarah E Berry, Kate Bermingham, Paul W Franks, Jonathan Wolf, Tim D Spector, Ana M Valdes, Phil Chowienczyk, Cristina Menni

Abstract

Postprandial insulinaemia, triglyceridaemia and measures of inflammation are thought to be more closely associated with cardiovascular risk than fasting measures. Although hypertension is associated with altered fasting metabolism, it is unknown as to what extent postprandial lipaemic and inflammatory metabolic responses differ between hypertensive and normotensive individuals. Linear models adjusting for age, sex, body mass index (BMI), visceral fat mass (VFM) and multiple testing (false discovery rate), were used to investigate whether hypertensive cases and normotensive controls had different fasting and postprandial (in response to two standardised test meal challenges) lipaemic, glycaemic, insulinaemic, and inflammatory (glycoprotein acetylation (GlycA)) responses in 989 participants from the ZOE PREDICT-1 nutritional intervention study. Compared to normotensive controls, hypertensive individuals had significantly higher fasting and postprandial insulin, triglycerides, and markers of inflammation after adjusting for age, sex, and BMI (effect size: Beta (Standard Error) ranging from 0.17 (0.08), p = 0.04 for peak insulin to 0.29 (0.08), p = 4.4 × 10-4 for peak GlycA). No difference was seen for postprandial glucose. When further adjusting for VFM effects were attenuated. Causal mediation analysis suggests that 36% of the variance in postprandial insulin response and 33.8% of variance in postprandial triglyceride response were mediated by VFM. Hypertensive individuals have different postprandial insulinaemic and lipaemic responses compared to normotensive controls and this is partially mediated by visceral fat mass. Consequently, reducing VFM should be a key focus of health interventions in hypertension. Trial registration: The ClinicalTrials.gov registration identifier is NCT03479866.

Keywords: hypertension; inflammation; insulinaemia; postprandial; triglyceridaemia.

Conflict of interest statement

T.D.S. is a co-founder and shareholder of ZOE, A.M.V. and P.W.F. are consultants for and J.W. is an employee of ZOE. All other authors declare no competing financial interests.

Figures

Figure 1
Figure 1
Flow chart of study analytical pipeline and clinical visit timeline. Study population and design.
Figure 2
Figure 2
Circos bar plot with bars representing standardised coefficients of linear models between metrics and hypertension status with error bars representing standard error. Bars are colour coded based on covariates. Light blue bars indicate adjustment for age, sex, and BMI, while navy bars indicated adjustment for age, sex, BMI, and VFM. * FDR

Figure 3

Mediation analysis of the association…

Figure 3

Mediation analysis of the association between hypertension and peak postprandial insulin and triglyceride…

Figure 3
Mediation analysis of the association between hypertension and peak postprandial insulin and triglyceride response via visceral fat. Path coefficients are illustrated beside each path and indirect effect and variance accounted for score is denoted below the mediator. Abbreviations: TG, triglycerides; HTN, hypertension; VAF, variance accounted for.
Figure 3
Figure 3
Mediation analysis of the association between hypertension and peak postprandial insulin and triglyceride response via visceral fat. Path coefficients are illustrated beside each path and indirect effect and variance accounted for score is denoted below the mediator. Abbreviations: TG, triglycerides; HTN, hypertension; VAF, variance accounted for.

References

    1. Mills K.T., Stefanescu A., He J. The Global Epidemiology of Hypertension. Nat. Rev. Nephrol. 2020;16:223–237. doi: 10.1038/s41581-019-0244-2.
    1. Mulè G., Calcaterra I., Nardi E., Cerasola G., Cottone S. Metabolic Syndrome in Hypertensive Patients: An Unholy Alliance. World J. Cardiol. 2014;6:890–907. doi: 10.4330/wjc.v6.i9.890.
    1. Katsimardou A., Imprialos K., Stavropoulos K., Sachinidis A., Doumas M., Athyros V. Hypertension in Metabolic Syndrome: Novel Insights. Curr. Hypertens. Rev. 2020;16:12–18. doi: 10.2174/1573402115666190415161813.
    1. Mancia G., Bombelli M., Corrao G., Facchetti R., Madotto F., Giannattasio C., Trevano F.Q., Grassi G., Zanchetti A., Sega R. Metabolic Syndrome in the Pressioni Arteriose Monitorate E Loro Associazioni (PAMELA) Study. Hypertension. 2007;49:40–47. doi: 10.1161/01.HYP.0000251933.22091.24.
    1. Schillaci G., Pirro M., Vaudo G., Gemelli F., Marchesi S., Porcellati C., Mannarino E. Prognostic Value of the Metabolic Syndrome in Essential Hypertension. J. Am. Coll. Cardiol. 2004;43:1817–1822. doi: 10.1016/j.jacc.2003.12.049.
    1. Cuspidi C., Facchetti R., Bombelli M., Sala C., Tadic M., Grassi G., Mancia G. Risk of New-Onset Metabolic Syndrome Associated with White-Coat and Masked Hypertension: Data from a General Population. J. Hypertens. 2018;36:1833–1839. doi: 10.1097/HJH.0000000000001767.
    1. Ferrannini E., Natali A., Capaldo B., Lehtovirta M., Jacob S., H. Yki-Järvinen for the European Group for the Study of Insulin Resistance Insulin Resistance, Hyperinsulinemia, and Blood Pressure. Hypertension. 1997;30:1144–1149. doi: 10.1161/01.HYP.30.5.1144.
    1. Horita S., Seki G., Yamada H., Suzuki M., Koike K., Fujita T. Insulin Resistance, Obesity, Hypertension, and Renal Sodium Transport. Int. J. Hypertens. 2011;2011:391762. doi: 10.4061/2011/391762.
    1. Berry S.E., Valdes A.M., Drew D.A., Asnicar F., Mazidi M., Wolf J., Capdevila J., Hadjigeorgiou G., Davies R., Al Khatib H., et al. Human Postprandial Responses to Food and Potential for Precision Nutrition. Nat. Med. 2020;26:964–973. doi: 10.1038/s41591-020-0934-0.
    1. Lopez-Miranda J., Marin C. Frontiers in neuroscience dietary, physiological, and genetic impacts on postprandial lipid metabolism. In: Montmayeur J.P., le Coutre J., editors. Fat Detection: Taste, Texture, and Post Ingestive Effects. Taylor and Francis; Abingdon, UK: 2010.
    1. Bansal S., Buring J.E., Rifai N., Mora S., Sacks F.M., Ridker P.M. Fasting Compared with Nonfasting Triglycerides and Risk of Cardiovascular Events in Women. JAMA. 2007;298:309–316. doi: 10.1001/jama.298.3.309.
    1. Hwu C.M., Kwok C.F., Kuo C.S., Hsiao L.C., Lee Y.S., Wei M.J., Kao W.Y., Lee S.H., Ho L.T. Exacerbation of Insulin Resistance and Postprandial Triglyceride Response in Newly Diagnosed Hypertensive Patients with Hypertriglyceridaemia. J. Hum. Hypertens. 2002;16:487–493. doi: 10.1038/sj.jhh.1001426.
    1. Elffers T.W., de Mutsert R., Lamb H.J., de Roos A., Willems van Dijk K., Rosendaal F.R., Jukema J.W., Trompet S. Body Fat Distribution, in Particular Visceral Fat, is Associated with Cardiometabolic Risk Factors in Obese Women. PLoS ONE. 2017;12:e0185403. doi: 10.1371/journal.pone.0185403.
    1. Franczyk M.P., He M., Yoshino J. Removal of Epididymal Visceral Adipose Tissue Prevents Obesity-Induced Multi-Organ Insulin Resistance in Male Mice. J. Endocr. Soc. 2021;5:bvab024. doi: 10.1210/jendso/bvab024.
    1. Gabriely I., Ma X.H., Yang X.M., Atzmon G., Rajala M.W., Berg A.H., Scherer P., Rossetti L., Barzilai N. Removal of Visceral Fat Prevents Insulin Resistance and Glucose Intolerance of Aging: An Adipokine-Mediated Process? Diabetes. 2002;51:2951–2958. doi: 10.2337/diabetes.51.10.2951.
    1. Verdi S., Abbasian G., Bowyer R.C.E., Lachance G., Yarand D., Christofidou P., Mangino M., Menni C., Bell J.T., Falchi M., et al. TwinsUK: The UK Adult Twin Registry Update. Twin Res. Hum. Genet. 2019;22:523–529. doi: 10.1017/thg.2019.65.
    1. Ballout R.A., Remaley A.T. GlycA: A New Biomarker for Systemic Inflammation and Cardiovascular Disease (CVD) Risk Assessment. J. Lab. Precis. Med. 2020;5:17. doi: 10.21037/jlpm.2020.03.03.
    1. Otvos J.D., Shalaurova I., Wolak-Dinsmore J., Connelly M.A., Mackey R.H., Stein J.H., Tracy R.P. GlycA: A Composite Nuclear Magnetic Resonance Biomarker of Systemic Inflammation. Clin. Chem. 2015;61:714–723. doi: 10.1373/clinchem.2014.232918.
    1. Akinkuolie A.O., Buring J.E., Ridker P.M., Mora S. A Novel Protein Glycan Biomarker and Future Cardiovascular Disease Events. J. Am. Heart Assoc. 2014;3:e001221. doi: 10.1161/JAHA.114.001221.
    1. Connelly M.A., Otvos J.D., Shalaurova I., Playford M.P., Mehta N.N. GlycA, a Novel Biomarker of Systemic Inflammation and Cardiovascular Disease Risk. J. Transl. Med. 2017;15:219. doi: 10.1186/s12967-017-1321-6.
    1. Direk K., Cecelja M., Astle W., Chowienczyk P., Spector T.D., Falchi M., Andrew T. The Relationship between DXA-Based and Anthropometric Measures of Visceral Fat and Morbidity in Women. BMC Cardiovasc. Disord. 2013;13:25. doi: 10.1186/1471-2261-13-25.
    1. Bertin E., Marcus C., Ruiz J.C., Eschard J.P., Leutenegger M. Measurement of Visceral Adipose Tissue by DXA Combined with Anthropometry in Obese Humans. Int. J. Obes. 2000;24:263–270. doi: 10.1038/sj.ijo.0801121.
    1. Tingley D., Yamamoto T., Hirose K., Keele L., Imai K. Mediation: R Package for Causal Mediation Analysis. J. Stat. Softw. 2014;59:38. doi: 10.18637/jss.v059.i05.
    1. Qu H.-Q., Li Q., Rentfro A.R., Fisher-Hoch S.P., McCormick J.B. The Definition of Insulin Resistance Using HOMA-IR for Americans of Mexican Descent Using Machine Learning. PLoS ONE. 2011;6:e21041. doi: 10.1371/journal.pone.0021041.
    1. Saxton S.N., Clark B.J., Withers S.B., Eringa E.C., Heagerty A.M. Mechanistic Links between Obesity, Diabetes, and Blood Pressure: Role of Perivascular Adipose Tissue. Physiol. Rev. 2019;99:1701–1763. doi: 10.1152/physrev.00034.2018.
    1. Kolb H., Kempf K., Röhling M., Martin S. Insulin: Too Much of a Good Thing is Bad. BMC Med. 2020;18:224. doi: 10.1186/s12916-020-01688-6.
    1. Jackson K.G., Poppitt S.D., Minihane A.M. Postprandial Lipemia and Cardiovascular Disease Risk: Interrelationships Between Dietary, Physiological and Genetic Determinants. Atherosclerosis. 2012;220:22–33. doi: 10.1016/j.atherosclerosis.2011.08.012.
    1. Kolovou G.D., Daskalova D., Iraklianou S.A., Adamopoulou E.N., Pilatis N.D., Hatzigeorgiou G.C., Cokkinos D.V. Postprandial Lipemia in Hypertension. J. Am. Coll. Nutr. 2003;22:80–87. doi: 10.1080/07315724.2003.10719279.
    1. Mancusi C., Izzo R., di Gioia G., Losi M.A., Barbato E., Morisco C. Insulin Resistance the Hinge Between Hypertension and Type 2 Diabetes. High Blood Press Cardiovasc. Prev. 2020;27:515–526. doi: 10.1007/s40292-020-00408-8.
    1. Chaudhary K., Buddineni J.P., Nistala R., Whaley-Connell A. Resistant Hypertension in the High-Risk Metabolic Patient. Curr. Diab. Rep. 2011;11:41–46. doi: 10.1007/s11892-010-0155-x.
    1. Nickenig G., Röling J., Strehlow K., Schnabel P., Böhm M. Insulin Induces Upregulation of Vascular AT1 Receptor Gene Expression by Posttranscriptional Mechanisms. Circulation. 1998;98:2453–2460. doi: 10.1161/01.CIR.98.22.2453.
    1. Najjar S.M., Perdomo G. Hepatic Insulin Clearance: Mechanism and Physiology. Physiology. 2019;34:198–215. doi: 10.1152/physiol.00048.2018.
    1. Si Y., Wang A., Yang Y., Liu H., Gu S., Mu Y., Lyu Z. Fasting Blood Glucose and 2-h Postprandial Blood Glucose Predict Hypertension: A Report from the Reaction Study. Diabetes. 2021;12:1117–1128. doi: 10.1007/s13300-021-01019-9.
    1. Zhao Y., Liu L., Yang S., Liu G., Pan L., Gu C., Wang Y., Li D., Zhao R., Wu M. Mechanisms of Atherosclerosis Induced by Postprandial Lipemia. Front. Cardiovasc. Med. 2021;8:636947. doi: 10.3389/fcvm.2021.636947.
    1. Bouchard C. BMI, Fat Mass, Abdominal Adiposity and Visceral Fat: Where is the ‘Beef’? Int. J. Obes. 2007;31:1552–1553. doi: 10.1038/sj.ijo.0803653.
    1. Schiavon C.A., Bersch-Ferreira A.C., Santucci E.V., Oliveira J.D., Torreglosa C.R., Bueno P.T., Frayha J.C., Santos R.N., Damiani L.P., Noujaim P.M., et al. Effects of Bariatric Surgery in Obese Patients with Hypertension: The Gateway Randomized Trial (Gastric Bypass to Treat Obese Patients with Steady Hypertension) Circulation. 2018;137:1132–1142. doi: 10.1161/CIRCULATIONAHA.117.032130.
    1. Pickering T.G. Measurement of Blood Pressure in and Out of the Office. J. Clin. Hypertens. 2005;7:123–129. doi: 10.1111/j.1524-6175.2005.04093.x.

Source: PubMed

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