Nutrition and Health (including climate and ecological aspects)

Secular changes in mid-adulthood body mass index, waist circumference, and low HDL cholesterol between 1990, 2003, and 2018 in Great Britain



To investigate the extent to which (1) secular changes in mid-adulthood WC are independent of BMI and (2) secular changes in low HDL-C are dependent on WC in each sex.


The sample comprised 19,406 adults (aged 43–47 years) from three birth cohort studies with BMI and WC measured in 1990, 2003, or 2018; 13,239 participants additionally had HDL-C measured in 2003 or 2018. Quantile regression was used to model differences between 1990–2003 and 2003–2018 in (1) BMI and WC internal Z-scores and (2) WC in cm before and after adjustment for BMI. Binary logistic regression was used to model differences between 2003 and 2018 in low HDL-C, before and after adjustment for BMI or WC.


Secular increases in BMI and WC were larger between 1990 and 2003 than 2003 and 2018 and at the upper ends of the distributions. At the 85th quantile, effect sizes were larger for WC than BMI Z-scores in females but not males. Adjustment for BMI attenuated estimates of secular increases in WC in cm more in males than females. Odds ratios for low HDL-C in 2018 compared to 2003 were 1.73 (95% CI 1.32, 2.28) in males and 1.34 (1.01, 1.78) in females. Adjustment for WC did not substantially change the estimate in males but attenuated the estimate for females to 1.09 (0.81, 1.47).


In women much more so than in men, secular increases in mid-adulthood WC appear to have occurred independently of BMI and largely explain the observed rise in low HDL-C prevalence between 2003 and 2018.

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  1. 1.

    NCD Risk Factor Collaboration. Trends in adult body-mass index in 200 countries from 1975 to 2014: a pooled analysis of 1698 population-based measurement studies with 19.2 million participants. Lancet. 2016;387:1377–96.

    Google Scholar 

  2. 2.

    NCD Risk Factor Collaboration. Worldwide trends in body-mass index, underweight, overweight, and obesity from 1975 to 2016: a pooled analysis of 2416 population-based measurement studies in 128.9 million children, adolescents, and adults. Lancet. 2017;390:2627–42.

    Google Scholar 

  3. 3.

    Romero-Corral A, Somers VK, Sierra-Johnson J, Thomas RJ, Collazo-Clavell ML, Korinek J, et al. Accuracy of body mass index in diagnosing obesity in the adult general population. Int J Obes. 2008;32:959–66.

    CAS  Google Scholar 

  4. 4.

    Prentice AM, Jebb SA. Beyond body mass index. Obes Rev. 2001;2:141–7.

    CAS  PubMed  Google Scholar 

  5. 5.

    Despres JP, Lemieux I, Bergeron J, Pibarot P, Mathieu P, Larose E, et al. Abdominal obesity and the metabolic syndrome: contribution to global cardiometabolic risk. Arterioscler Thromb Vasc Biol. 2008;28:1039–49.

    CAS  PubMed  Google Scholar 

  6. 6.

    Despres JP. Is visceral obesity the cause of the metabolic syndrome? Ann Med. 2006;38:52–63.

    CAS  PubMed  Google Scholar 

  7. 7.

    Camhi SM, Bray GA, Bouchard C, Greenway FL, Johnson WD, Newton RL. et al. The relationship of waist circumference and BMI to visceral, subcutaneous, and total body fat: sex and race differences. Obesity. 2011;19:402–8.

    PubMed  Google Scholar 

  8. 8.

    Janssen I, Heymsfield SB, Allison DB, Kotler DP, Ross R. Body mass index and waist circumference independently contribute to the prediction of nonabdominal, abdominal subcutaneous, and visceral fat. Am J Clin Nutr. 2002;75:683–8.

    CAS  PubMed  Google Scholar 

  9. 9.

    Carmienke S, Freitag MH, Pischon T, Schlattmann P, Fankhaenel T, Goebel H, et al. General and abdominal obesity parameters and their combination in relation to mortality: a systematic review and meta-regression analysis. Eur J Clin Nutr. 2013;67:573–85.

    CAS  PubMed  Google Scholar 

  10. 10.

    de Hollander EL, Bemelmans WJ, Boshuizen HC, Friedrich N, Wallaschofski H, Guallar-Castillon P, et al. The association between waist circumference and risk of mortality considering body mass index in 65- to 74-year-olds: a meta-analysis of 29 cohorts involving more than 58 000 elderly persons. Int J Epidemiol. 2012;41:805–17.

    PubMed  PubMed Central  Google Scholar 

  11. 11.

    Decoda Study G, Nyamdorj R, Qiao Q, Lam TH, Tuomilehto J, Ho SY. et al. BMI compared with central obesity indicators in relation to diabetes and hypertension in Asians. Obesity. 2018;16:1622–35.

    Google Scholar 

  12. 12.

    Hamer M, O’Donovan G, Stensel D, Stamatakis E. Normal-weight central obesity and risk for mortality. Ann Intern Med. 2017;166:917–8.

    PubMed  Google Scholar 

  13. 13.

    Kodama S, Horikawa C, Fujihara K, Heianza Y, Hirasawa R, Yachi Y, et al. Comparisons of the strength of associations with future type 2 diabetes risk among anthropometric obesity indicators, including waist-to-height ratio: a meta-analysis. Am J Epidemiol. 2012;176:959–69.

    PubMed  Google Scholar 

  14. 14.

    Flegal KM, Shepherd JA, Looker AC, Graubard BI, Borrud LG, Ogden CL, et al. Comparisons of percentage body fat, body mass index, waist circumference, and waist-stature ratio in adults. Am J Clin Nutr. 2009;89:500–8.

    CAS  PubMed  Google Scholar 

  15. 15.

    Albrecht SS, Gordon-Larsen P, Stern D, Popkin BM. Is waist circumference per body mass index rising differentially across the United States, England, China and Mexico? Eur J Clin Nutr. 2015;69:1306–12.

    CAS  PubMed  PubMed Central  Google Scholar 

  16. 16.

    Elobeid MA, Desmond RA, Thomas O, Keith SW, Allison DB. Waist circumference values are increasing beyond those expected from BMI increases. Obesity. 2007;15:2380–3.

    PubMed  Google Scholar 

  17. 17.

    Ford ES, Mokdad AH, Giles WH. Trends in waist circumference among U.S. adults. Obes Res. 2003;11:1223–31.

    PubMed  Google Scholar 

  18. 18.

    Freedman DS, Ford ES. Are the recent secular increases in the waist circumference of adults independent of changes in BMI? Am J Clin Nutr. 2015;101:425–31.

    CAS  PubMed  PubMed Central  Google Scholar 

  19. 19.

    Visscher TL, Heitmann BL, Rissanen A, Lahti-Koski M, Lissner L. A break in the obesity epidemic? Explained by biases or misinterpretation of the data? Int J Obes. 2015;39:189–98.

    CAS  Google Scholar 

  20. 20.

    Walls HL, Stevenson CE, Mannan HR, Abdullah A, Reid CM, McNeil JJ, et al. Comparing trends in BMI and waist circumference. Obesity. 2011;19:216–9.

    PubMed  Google Scholar 

  21. 21.

    Rashid S, Genest J. Effect of obesity on high-density lipoprotein metabolism. Obesity. 2007;15:2875–88.

    CAS  PubMed  Google Scholar 

  22. 22.

    Gordon T, Castelli WP, Hjortland MC, Kannel WB, Dawber TR. High density lipoprotein as a protective factor against coronary heart disease. The Framingham Study. Am J Med. 1977;62:707–14.

    CAS  PubMed  Google Scholar 

  23. 23.

    Gordon T, Castelli WP, Hjortland MC, Kannel WB, Dawber TR. Diabetes, blood lipids, and the role of obesity in coronary heart disease risk for women. The Framingham study. Ann Intern Med. 1977;87:393–7.

    CAS  PubMed  Google Scholar 

  24. 24.

    Brenner DR, Tepylo K, Eny KM, Cahill LE, El-Sohemy A. Comparison of body mass index and waist circumference as predictors of cardiometabolic health in a population of young Canadian adults. Diabetol Metab Syndr. 2010;2:28.

    PubMed  PubMed Central  Google Scholar 

  25. 25.

    Shen W, Punyanitya M, Chen J, Gallagher D, Albu J, Pi-Sunyer X, et al. Waist circumference correlates with metabolic syndrome indicators better than percentage fat. Obesity. 2006;14:727–36.

    PubMed  Google Scholar 

  26. 26.

    Schreiner PJ, Jacobs DR, Wong ND, Kiefe CI. Twenty-five year secular trends in lipids and modifiable risk factors in a population-based biracial cohort: the coronary artery risk development in young adults (CARDIA) Study, 1985–2011. J Am Heart Assoc. 2016;5:e003384.

    PubMed  PubMed Central  Google Scholar 

  27. 27.

    Johnson W, Li L, Kuh D, Hardy R. How has the age-related process of overweight or obesity development changed over time? Co-ordinated analyses of individual participant data from five United Kingdom birth cohorts. PLoS Med. 2015;12:e1001828.

    PubMed  PubMed Central  Google Scholar 

  28. 28.

    Kuh D, Pierce M, Adams J, Deanfield J, Ekelund U, Friberg P, et al. Cohort profile: updating the cohort profile for the MRC National Survey of Health and Development: a new clinic-based data collection for ageing research. Int J Epidemiol. 2011;40:e1–9.

    PubMed  PubMed Central  Google Scholar 

  29. 29.

    Wadsworth M, Kuh D, Richards M, Hardy R. Cohort profile: The 1946 National Birth Cohort (MRC National Survey of Health and Development). Int J Epidemiol. 2006;35:49–54.

    PubMed  Google Scholar 

  30. 30.

    Power C, Elliott J. Cohort profile: 1958 British birth cohort (National Child Development Study). Int J Epidemiol. 2006;35:34–41.

    PubMed  Google Scholar 

  31. 31.

    Elliott J, Shepherd P. Cohort profile: 1970 British Birth Cohort (BCS70). Int J Epidemiol. 2006;35:836–43.

    PubMed  Google Scholar 

  32. 32.

    Grundy SM, Cleeman JI, Daniels SR, Donato KA, Eckel RH, Franklin BA, et al. Diagnosis and management of the metabolic syndrome: an American Heart Association/National Heart, Lung, and Blood Institute Scientific Statement. Circulation. 2005;112:2735–52.

    PubMed  Google Scholar 

  33. 33.

    Expert Panel on Detection Evaluation and Treatment of High Blood Cholesterol in Adults. Executive Summary of The Third Report of The National Cholesterol Education Program (NCEP) expert panel on detection, evaluation, and treatment of high blood cholesterol in adults (Adult Treatment Panel III). JAMA. 2001;285:2486–97.

    Google Scholar 

  34. 34.

    Bann D, Fitzsimons E, Johnson W. Determinants of the population health distribution: an illustration examining body mass index. Int J Epidemiol. 2020;49:731–7.

    PubMed  PubMed Central  Google Scholar 

  35. 35.

    Royston P, Ambler G, Sauerbrei W. The use of fractional polynomials to model continuous risk variables in epidemiology. Int J Epidemiol. 1999;28:964–74.

    CAS  PubMed  Google Scholar 

  36. 36.

    Craig WY, Palomaki GE, Haddow JE. Cigarette smoking and serum lipid and lipoprotein concentrations: an analysis of published data. BMJ. 1989;298:784–8.

    CAS  PubMed  PubMed Central  Google Scholar 

  37. 37.

    Freeman DJ, Griffin BA, Murray E, Lindsay GM, Gaffney D, Packard CJ, et al. Smoking and plasma lipoproteins in man: effects on low density lipoprotein cholesterol levels and high density lipoprotein subfraction distribution. Eur J Clin Investig. 1993;23:630–40.

    CAS  Google Scholar 

  38. 38.

    Giskes K, Kunst AE, Benach J, Borrell C, Costa G, Dahl E, et al. Trends in smoking behaviour between 1985 and 2000 in nine European countries by education. J Epidemiol Community Health. 2005;59:395–401.

    CAS  PubMed  PubMed Central  Google Scholar 

  39. 39.

    Ng M, Freeman MK, Fleming TD, Robinson M, Dwyer-Lindgren L, Thomson B, et al. Smoking prevalence and cigarette consumption in 187 countries, 1980-2012. JAMA. 2014;311:183–92.

    CAS  PubMed  Google Scholar 

  40. 40.

    Freedman DS, Zemel BS, Ogden CL. Secular trends for skinfolds differ from those for BMI and waist circumference among adults examined in NHANES from 1988-1994 through 2009-2010. Am J Clin Nutr. 2017;105:169–76.

    CAS  PubMed  Google Scholar 

  41. 41.

    Primatesta P, Poulter NR. Levels of dyslipidaemia and improvement in its management in England: results from the Health Survey for England 2003. Clin Endocrinol. 2006;64:292–8.

    Google Scholar 

  42. 42.

    The Scottish Government. The Scottish Health Survey. Edinburgh: The Scottish Government; 2003.

    Google Scholar 

  43. 43.

    Mindell J, Aresu M, Zaninotto P, Falaschetti E, Poulter N. Improving lipid profiles and increasing use of lipid-lowering therapy in England: results from a national cross-sectional survey—2006. Clin Endocrinol. 2011;75:621–7.

    CAS  Google Scholar 

  44. 44.

    NCD Risk Factor Collaboration. Repositioning of the global epicentre of non-optimal cholesterol. Nature. 2020;582:73–7.

    Google Scholar 

  45. 45.

    Kissebah AH, Vydelingum N, Murray R, Evans DJ, Hartz AJ, Kalkhoff RK, et al. Relation of body fat distribution to metabolic complications of obesity. J Clin Endocrinol Metab. 1982;54:254–60.

    CAS  PubMed  Google Scholar 

  46. 46.

    Kissebah AH, Alfarsi S, Adams PW. Integrated regulation of very low density lipoprotein triglyceride and apolipoprotein-B kinetics in man: normolipemic subjects, familial hypertriglyceridemia and familial combined hyperlipidemia. Metabolism. 1981;30:856–68.

    CAS  PubMed  Google Scholar 

  47. 47.

    Peiris AN, Sothmann MS, Hoffmann RG, Hennes MI, Wilson CR, Gustafson AB, et al. Adiposity, fat distribution, and cardiovascular risk. Ann Intern Med. 1989;110:867–72.

    CAS  PubMed  Google Scholar 

  48. 48.

    Munafo MR, Tilling K, Taylor AE, Evans DM, Davey Smith G. Collider scope: when selection bias can substantially influence observed associations. Int J Epidemiol. 2018;47:226–35.

    PubMed  Google Scholar 

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This work was funded by the UK Medical Research Council (WJ New Investigator Research Grant: MR/P023347/1). WJ acknowledges support from the National Institute for Health Research (NIHR) Leicester Biomedical Research Centre, which is a partnership between University Hospitals of Leicester NHS Trust, Loughborough University, and the University of Leicester. The UK Medical Research Council provides core funding for the MRC National Survey of Health and Development. WJ conceptualised the study, carried out the analyses, and drafted the initial paper. All authors made substantial contributions to the interpretation of the data, revised the paper critically for important intellectual content, gave final approval of the version to be published, and agree to be accountable for all aspects of the work.

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Johnson, W., Norris, T. & Hamer, M. Secular changes in mid-adulthood body mass index, waist circumference, and low HDL cholesterol between 1990, 2003, and 2018 in Great Britain. Eur J Clin Nutr (2020).

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