Gender-specific thresholds and nonlinear associations of obesity indices with hyperlipidemia: insights from NHANES 2007–2020

Research Article
Open access

Gender-specific thresholds and nonlinear associations of obesity indices with hyperlipidemia: insights from NHANES 2007–2020

Yaning Tong 1 , Liang Ma 2 , Fangzhu Xiao 3*
  • 1 School of Public Health, University of South China, Hengyang, China     
  • 2 School of Public Health, University of South China, Hengyang, China     
  • 3 School of Public Health, University of South China, Hengyang, China     
  • *corresponding author xfzhunh@163.com
JCTT Vol.3 Issue 2
ISSN (Print): 3049-5466
ISSN (Online): 3049-5458

Abstract

Hyperlipidemia, a significant contributor to cardiovascular ailments, have a close association with obesity, is closely linked to obesity. Traditional anthropometric indices (BMI, WHR, WHtR) are widely used to assess obesity, but their nonlinear relationships with hyperlipidemia and gender-specific risk thresholds remain poorly understood. Leveraging data from 2,739 participants in the NHANES 2007–2020, this research utilized multivariable logistic regression analysis along with restricted cubic splines (RCS) to investigate both linear and nonlinear relationships between various obesity indices and the presence of hyperlipidemia. Nonlinear dose-response relationships were observed for BMI, WHR, and WHtR with hyperlipidemia (P for nonlinear < 0.001). Gender-specific risk thresholds were identified: males: WHR > 0.94, WHtR > 0.76, BMI >28.0; Females: WHR 0.91–0.99, WHtR 0.61–0.69, BMI 29.4–34.39. Gender differences in effect sizes were significant (e.g., WHR β: males 6.986, P<0.001 vs. females 5.666, P=0.023). Subgroup analysis showed stronger associations in younger individuals (≤50 years) without hypertension or diabetes. WHR remained an independent predictor in metabolic comorbidities, while BMI and WHtR were confounded by metabolic disturbances. This study highlights the importance of gender-specific risk thresholds for obesity indices, particularly WHR, in predicting hyperlipidemia. The results demonstrate that WHR serves as a more reliable predictor for metabolic disorder risks compared to other anthropometric indices, particularly highlighting the importance of implementing preventive measures in populations under 50 years of age. Future research should focus on developing precision stratification models to optimize obesity management.

Keywords:

hyperlipidemia, obesity indices, Body Mass Index (BMI), Waist-to-Hip Ratio (WHR), Waist-to-Height Ratio (WHtR)

Tong,Y.;Ma,L.;Xiao,F. (2025). Gender-specific thresholds and nonlinear associations of obesity indices with hyperlipidemia: insights from NHANES 2007–2020. Journal of Clinical Technology and Theory,3(2),15-22.
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References

[1]. Mensah, G. A., Roth, G. A., & Fuster, V. (2019). The global burden of cardiovascular diseases and risk factors: 2020 and beyond. American College of Cardiology Foundation Washington, DC, 2529–2532.

[2]. Jung, U. J., & Choi, M.-S. (2014). Obesity and its metabolic complications: The role of adipokines and the relationship between obesity, inflammation, insulin resistance, dyslipidemia and nonalcoholic fatty liver disease. International Journal of Molecular Sciences, 15(4), 6184–6223.

[3]. Piqueras, P., Ballester, A., Durá-Gil, J. V., Martinez-Hervas, S., Redón, J., & Real, J. T. (2021). Anthropometric indicators as a tool for diagnosis of obesity and other health risk factors: A literature review. Frontiers in Psychology, 12, 631179.

[4]. Després, J.-P. (2012). Body fat distribution and risk of cardiovascular disease: An update. Circulation, 126(10), 1301–1313.

[5]. Amato, M. C., Giordano, C., Galia, M., Criscimanna, A., Vitabile, S., Midiri, M., & Galluzzo, A. (2010). Visceral adiposity index: A reliable indicator of visceral fat function associated with cardiometabolic risk. Diabetes Care, 33(4), 920–922.

[6]. Moltrer, M., Pala, L., Cosentino, C., Mannucci, E., Rotella, C., & Cresci, B. (2022). Body mass index (BMI), waist circumference (WC), waist-to-height ratio (WHtR) e waist body mass index (WBMI): Which is better? Endocrine, 76(3), 578–583.

[7]. Padwal R, Leslie WD, Lix LM, Majumdar SR. (2016). Relationship Among Body Fat Percentage, Body Mass Index, and All-Cause Mortality: A Cohort Study. Ann Intern Med. Apr 19;164(8):532-41. doi: 10.7326/M15-1181.

[8]. Lam, B. C. C., Koh, G. C. H., Chen, C., Wong, M. T. K., & Fallows, S. J. (2015). Comparison of body mass index (BMI), body adiposity index (BAI), waist circumference (WC), waist-to-hip ratio (WHR) and waist-to-height ratio (WHtR) as predictors of cardiovascular disease risk factors in an adult population in Singapore. PLOS ONE, 10(4), e0122985.

[9]. Rao, W., Su, Y., Yang, G., Ma, Y., Liu, R., Zhang, S., Wang, S., Fu, Y., Kou, C., & Yu, Y. (2016). Cross-sectional associations between body mass index and hyperlipidemia among adults in northeastern China. International Journal of Environmental Research and Public Health, 13(5), 516.

[10]. Yang, L., Liu, J., Xing, Y., Du, L., Chen, J., Liu, X., & Hao, J. (2017). Correlation of body mass index and waist‐hip ratio with severity and complications of hyperlipidemic acute pancreatitis in Chinese patients. Gastroenterology Research and Practice, 67, 57805.

[11]. Dong, J., Ni, Y.-Q., Chu, X., Liu, Y.-Q., Liu, G.-X., Zhao, J., Ren, L., Hu, J.-W., Liu, J., & He, S.-S. (2016). Association between the abdominal obesity anthropometric indicators and metabolic disorders in a Chinese population. Public Health, 131, 3–10.

[12]. Karastergiou, K., Smith, S. R., Greenberg, A. S., & Fried, S. K. (2012). Sex differences in human adipose tissues–the biology of pear shape. Biology of Sex Differences, 3(1), 13.

[13]. Palmer, B. F., & Clegg, D. J. (2015). The sexual dimorphism of obesity. Molecular and Cellular Endocrinology, 402, 113–119.

[14]. Friedewald, W. T., Levy, R. I., & Fredrickson, D. S. (1972). Estimation of the concentration of low-density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge. Clinical Chemistry, 18(6), 499–502.

[15]. Cleeman, J. I. (2001). 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, 285(19), 2486–2497.

[16]. Sullivan, P. W., Ghushchyan, V. H., & Ben-Joseph, R. (2008). The impact of obesity on diabetes, hyperlipidemia and hypertension in the United States. Quality of Life Research, 17(8), 1063–1071.

[17]. Harrell, F. E. (2001). Regression modeling strategies. Springer.

[18]. Nunez, E., Steyerberg, E. W., & Nunez, J. (2011). Regression modeling strategies. Revista Española de Cardiología (English Edition), 64(6), 501–507.

[19]. Kishida, K., Funahashi, T., & Shimomura, I. (2012). Molecular mechanisms of diabetes and atherosclerosis: Role of adiponectin. Endocrine. Metabolic & Immune Disorders - Drug Targets, 12(2), 118–131.

[20]. Pham, D.-V., & Park, P.-H. (2020). Recent insights on modulation of inflammasomes by adipokines: A critical event for the pathogenesis of obesity and metabolism-associated diseases. Archives of Pharmacal Research, 43(11), 997–1016.

[21]. Nauli, A. M., & Matin, S. (2019). Why do men accumulate abdominal visceral fat? Frontiers in Physiology, 10, 1486.

[22]. Steiner, B. M., & Berry, D. C. (2022). The regulation of adipose tissue health by estrogens. Frontiers in Endocrinology, 13, 889923.

[23]. Palmisano, B. T., Zhu, L., Eckel, R. H., & Stafford, J. M. (2018). Sex differences in lipid and lipoprotein metabolism. Molecular Metabolism, 15, 45–55.

[24]. Russo, G. T., Giandalia, A., Romeo, E. L., & Cucinotta, D. (2015). Gender differences in lipoprotein metabolism. Journal of Sex- and Gender-Specific Medicine, 1(2), 58–65.

[25]. Lolli, F. (2018). ERalpha: The crossroad between metabolic fitness and metabolic illness. Journal of Molecular Endocrinology, 61(1), R1–R6.

[26]. Larsson, L., Degens, H., Li, M., Salviati, L., Lee, Y. I., Thompson, W., Kirkland, J. L., & Sandri, M. (2019). Sarcopenia: Aging-related loss of muscle mass and function. Physiological Reviews, 99(1), 427–511.

[27]. Ross, R., Neeland, I. J., Yamashita, S., Shai, I., Seidell, J., Magni, P., Santos, R. D., Arsenault, B., Cuevas, A., Hu, F. B., Griffin, B. A., Zambon, A., Barter, P., Fruchart, J.-C., Eckel, R. H., Matsuzawa, Y., & Després, J.-P. (2020). Waist circumference as a vital sign in clinical practice: A consensus statement from the IAS and ICCR Working Group on Visceral Obesity. Nature Reviews Endocrinology, 16(3), 177–189.

[28]. Koenen, M., Hill, M. A., Cohen, P., & Sowers, J. R. (2021). Obesity, adipose tissue and vascular dysfunction. Circulation Research, 128(7), 951–968. https://doi.org/10.1161/circresaha.121.318093

[29]. Butler, R., McClinchy, J., Morreale-Parker, C., Marsh, W., & Rennie, K. L. (2017). BMI calculation in older people: The effect of using direct and surrogate measures of height in a community-based setting. Clinical Nutrition ESPEN, 22, 112–115. https://doi.org/10.1016/j.clnesp.2017.07.078

Cite this article

Tong,Y.;Ma,L.;Xiao,F. (2025). Gender-specific thresholds and nonlinear associations of obesity indices with hyperlipidemia: insights from NHANES 2007–2020. Journal of Clinical Technology and Theory,3(2),15-22.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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Journal：Journal of Clinical Technology and Theory

Volume number: Vol.3
Issue number: Issue 2
ISSN：3049-5458(Print) / 3049-5466(Online)

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References

[1]. Mensah, G. A., Roth, G. A., & Fuster, V. (2019). The global burden of cardiovascular diseases and risk factors: 2020 and beyond. American College of Cardiology Foundation Washington, DC, 2529–2532.

[2]. Jung, U. J., & Choi, M.-S. (2014). Obesity and its metabolic complications: The role of adipokines and the relationship between obesity, inflammation, insulin resistance, dyslipidemia and nonalcoholic fatty liver disease. International Journal of Molecular Sciences, 15(4), 6184–6223.

[3]. Piqueras, P., Ballester, A., Durá-Gil, J. V., Martinez-Hervas, S., Redón, J., & Real, J. T. (2021). Anthropometric indicators as a tool for diagnosis of obesity and other health risk factors: A literature review. Frontiers in Psychology, 12, 631179.

[4]. Després, J.-P. (2012). Body fat distribution and risk of cardiovascular disease: An update. Circulation, 126(10), 1301–1313.

[5]. Amato, M. C., Giordano, C., Galia, M., Criscimanna, A., Vitabile, S., Midiri, M., & Galluzzo, A. (2010). Visceral adiposity index: A reliable indicator of visceral fat function associated with cardiometabolic risk. Diabetes Care, 33(4), 920–922.

[6]. Moltrer, M., Pala, L., Cosentino, C., Mannucci, E., Rotella, C., & Cresci, B. (2022). Body mass index (BMI), waist circumference (WC), waist-to-height ratio (WHtR) e waist body mass index (WBMI): Which is better? Endocrine, 76(3), 578–583.

[7]. Padwal R, Leslie WD, Lix LM, Majumdar SR. (2016). Relationship Among Body Fat Percentage, Body Mass Index, and All-Cause Mortality: A Cohort Study. Ann Intern Med. Apr 19;164(8):532-41. doi: 10.7326/M15-1181.

[8]. Lam, B. C. C., Koh, G. C. H., Chen, C., Wong, M. T. K., & Fallows, S. J. (2015). Comparison of body mass index (BMI), body adiposity index (BAI), waist circumference (WC), waist-to-hip ratio (WHR) and waist-to-height ratio (WHtR) as predictors of cardiovascular disease risk factors in an adult population in Singapore. PLOS ONE, 10(4), e0122985.

[9]. Rao, W., Su, Y., Yang, G., Ma, Y., Liu, R., Zhang, S., Wang, S., Fu, Y., Kou, C., & Yu, Y. (2016). Cross-sectional associations between body mass index and hyperlipidemia among adults in northeastern China. International Journal of Environmental Research and Public Health, 13(5), 516.

[10]. Yang, L., Liu, J., Xing, Y., Du, L., Chen, J., Liu, X., & Hao, J. (2017). Correlation of body mass index and waist‐hip ratio with severity and complications of hyperlipidemic acute pancreatitis in Chinese patients. Gastroenterology Research and Practice, 67, 57805.

[11]. Dong, J., Ni, Y.-Q., Chu, X., Liu, Y.-Q., Liu, G.-X., Zhao, J., Ren, L., Hu, J.-W., Liu, J., & He, S.-S. (2016). Association between the abdominal obesity anthropometric indicators and metabolic disorders in a Chinese population. Public Health, 131, 3–10.

[12]. Karastergiou, K., Smith, S. R., Greenberg, A. S., & Fried, S. K. (2012). Sex differences in human adipose tissues–the biology of pear shape. Biology of Sex Differences, 3(1), 13.

[13]. Palmer, B. F., & Clegg, D. J. (2015). The sexual dimorphism of obesity. Molecular and Cellular Endocrinology, 402, 113–119.

[14]. Friedewald, W. T., Levy, R. I., & Fredrickson, D. S. (1972). Estimation of the concentration of low-density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge. Clinical Chemistry, 18(6), 499–502.

[15]. Cleeman, J. I. (2001). 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, 285(19), 2486–2497.

[16]. Sullivan, P. W., Ghushchyan, V. H., & Ben-Joseph, R. (2008). The impact of obesity on diabetes, hyperlipidemia and hypertension in the United States. Quality of Life Research, 17(8), 1063–1071.

[17]. Harrell, F. E. (2001). Regression modeling strategies. Springer.

[18]. Nunez, E., Steyerberg, E. W., & Nunez, J. (2011). Regression modeling strategies. Revista Española de Cardiología (English Edition), 64(6), 501–507.

[19]. Kishida, K., Funahashi, T., & Shimomura, I. (2012). Molecular mechanisms of diabetes and atherosclerosis: Role of adiponectin. Endocrine. Metabolic & Immune Disorders - Drug Targets, 12(2), 118–131.

[20]. Pham, D.-V., & Park, P.-H. (2020). Recent insights on modulation of inflammasomes by adipokines: A critical event for the pathogenesis of obesity and metabolism-associated diseases. Archives of Pharmacal Research, 43(11), 997–1016.

[21]. Nauli, A. M., & Matin, S. (2019). Why do men accumulate abdominal visceral fat? Frontiers in Physiology, 10, 1486.

[22]. Steiner, B. M., & Berry, D. C. (2022). The regulation of adipose tissue health by estrogens. Frontiers in Endocrinology, 13, 889923.

[23]. Palmisano, B. T., Zhu, L., Eckel, R. H., & Stafford, J. M. (2018). Sex differences in lipid and lipoprotein metabolism. Molecular Metabolism, 15, 45–55.

[24]. Russo, G. T., Giandalia, A., Romeo, E. L., & Cucinotta, D. (2015). Gender differences in lipoprotein metabolism. Journal of Sex- and Gender-Specific Medicine, 1(2), 58–65.

[25]. Lolli, F. (2018). ERalpha: The crossroad between metabolic fitness and metabolic illness. Journal of Molecular Endocrinology, 61(1), R1–R6.

[26]. Larsson, L., Degens, H., Li, M., Salviati, L., Lee, Y. I., Thompson, W., Kirkland, J. L., & Sandri, M. (2019). Sarcopenia: Aging-related loss of muscle mass and function. Physiological Reviews, 99(1), 427–511.

[27]. Ross, R., Neeland, I. J., Yamashita, S., Shai, I., Seidell, J., Magni, P., Santos, R. D., Arsenault, B., Cuevas, A., Hu, F. B., Griffin, B. A., Zambon, A., Barter, P., Fruchart, J.-C., Eckel, R. H., Matsuzawa, Y., & Després, J.-P. (2020). Waist circumference as a vital sign in clinical practice: A consensus statement from the IAS and ICCR Working Group on Visceral Obesity. Nature Reviews Endocrinology, 16(3), 177–189.

[28]. Koenen, M., Hill, M. A., Cohen, P., & Sowers, J. R. (2021). Obesity, adipose tissue and vascular dysfunction. Circulation Research, 128(7), 951–968. https://doi.org/10.1161/circresaha.121.318093

[29]. Butler, R., McClinchy, J., Morreale-Parker, C., Marsh, W., & Rennie, K. L. (2017). BMI calculation in older people: The effect of using direct and surrogate measures of height in a community-based setting. Clinical Nutrition ESPEN, 22, 112–115. https://doi.org/10.1016/j.clnesp.2017.07.078

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