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Aging and disease
Orginal Article |
Trends in LDL-C and Non-HDL-C Levels with Age
Peng Zhang1, Qian Su1, Xiaomiao Ye1, Ping Guan1, Chengjun Chen1, Yanwen Hang1, Jian Dong1, Zhongjie Xu2,*, Wei Hu1,*
1Department of Cardiology, Minhang Hospital, Fudan University, Shanghai, China
2Shanghai Minhang District Medical Emergency Center, Shanghai, China
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Abstract  

Understanding how blood lipid levels change with age in the general population is a precondition to defining dyslipidemia. To explore age-related trends in LDL-C and non-HDL-C levels in the general population, a large-scale cross-sectional study with 49,201 males and 35,084 females was adopted. Trends of non-HDL-C and LDL-C levels were plotted against each age (18 to 85 years old, one-year increments); the trends, as well as the influence of confounding factors on the trends, were validated and adjusted by linear regression modeling. The trajectory of LDL-C and non-HDL-C levels by age displayed a nonlinear correlation trend. Further multivariate linear regression modeling that incorporated sex-specific age phases showed that age was positively associated with LDL-C and non-HDL-C levels, with coefficients of 0.018 and 0.031, respectively, in females aged ≥18 to ≤56 years and negatively associated with LDL-C and non-HDL-C levels, with coefficients of -0·013 and -0.015, respectively, in females aged ≥57 years. The LDL-C and non-HDL-C levels increased with age in males ≥18 to ≤33 years of age, with coefficients of 0.025 and 0.053, respectively; the lipid levels plateaued at ≥34 to ≤56 years of age and subsequently decreased in those ≥57 years of age, with coefficients of -0.008 and -0.018, respectively. In contrast, pooled analyses without age stratification concealed these details. In conclusion, fluctuating increasing and decreasing lipid levels occurred with phases of aging in both sexes. Well-grounded age stratification is necessary to improve lipid-related pathophysiological studies.

Keywords low-density lipoprotein cholesterol (LDL-C)      non-high-density lipoprotein cholesterol (non-HDL-C)      age     
Corresponding Authors: Zhongjie Xu,Wei Hu   
Just Accepted Date: 19 November 2019  
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Peng Zhang
Qian Su
Xiaomiao Ye
Ping Guan
Chengjun Chen
Yanwen Hang
Jian Dong
Zhongjie Xu
Wei Hu
Cite this article:   
Peng Zhang,Qian Su,Xiaomiao Ye, et al. Trends in LDL-C and Non-HDL-C Levels with Age[J]. Aging and disease, 10.14336/AD.2019.1025
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http://www.aginganddisease.org/EN/10.14336/AD.2019.1025     OR     http://www.aginganddisease.org/EN/Y/V/I/0
Figure 1.  Flow chart of the participants. A total of 92,687 subjects (54,322 males and 38,365 females) attended annual physical examinations, of whom 84,285 subjects (49,201 males and 35,084 females) with 168,570 person times data were included in the statistical analyses.
ParameterMaleFemaleP
Number of subjects49,20135,084/
Person-time98,40270,168/
Age, years44.3 ± 13.242.4 ± 12.5<0.001
Current smoker17,221 (35.0%)726 (2.1%)<0.001
Moderate drinker10,993 (22.3%)1,022 (2.9%)<0.001
Inactive lifestyle8,856 (18.0%)5,101 (14.8%)<0.001
BMI (kg/m2)24.4 ± 3.122.3 ± 3.1<0.001
Systolic BP132.4 ± 17.7125.0 ± 19.0<0.001
Diastolic BP81.8 ± 11.375.1 ± 11.2<0.001
TC (mmol/L)4.76 ± 0.914.59 ± 0.87<0.001
TG (mmol/L)1.73 (1.20, 2.56)1.17 (0.84, 1.68)<0.001
HDL-C (mmol/L)1.29 ± 0.381.49 ± 0.32<0.001
LDL-C (mmol/L)2.59 ± 0.822.46 ± 0.73<0.001
Non-HDL-C (mmol/L)3.48 ± 0.963.10 ± 0.85<0.001
Glucose (mmol/L)5.52 ± 1.45.23 ± 1.0<0.001
Table 1  General distribution characteristics of the parameters by sex.
Figure 2.  Lifetime trends of the LDL-C and non-HDL-C levels. Means with 95% CIs for LDL-C and non-HDL-C levels (left ordinate) are plotted against each age by sex; the shadow indicates the number of subjects at each age (right ordinate).
FemaleMale
VariableCoef.95% CIPVariableCoef.95% CIP
Pooled
Age0.0160.015~0.016<0.001Age0.0030.002~0.003<0.001
Systolic BP///Systolic BP///
Diastolic BP0.0070.003~0.012<0.001Diastolic BP0.0050.002~0.009<0.001
Glucose (mmol/L)0.0910.023~0.132<0.001Glucose (mmol/L)0.0820.045~0.1240.003
BMI (kg/m2)0.0310.022~0.0440.001BMI (kg/m2)0.0410.032~0.056<0.001
Inactive lifestyle0.0230.019~0.0380.002Inactive lifestyle0.0310.019~0.0440.003
Moderate drinking///Moderate drinking0.0440.032~0.0560.003
Smoking///Smoking0.0560.045~0.0770.001
Age-stratified
Age phase I: 18 ≤ age ≤ 56 (N = 61,420)Age phase I: 18 ≤ age ≤ 33 (N = 22,942)
Age0.0200.020~0.021<0.001Age0.0260.023~0.029<0.001
Systolic BP///Systolic BP///
Diastolic BP0.0040.001~0.008<0.001Diastolic BP///
Glucose (mmol/L)0.1140.098~0.1430.002Glucose (mmol/L)0.1540.137~0.1760.001
BMI (kg/m2)0.0390.028~0.0510.003BMI (kg/m2)0.0630.048~0.079<0.001
Inactive lifestyle0.0480.039~0.066<0.001Inactive lifestyle0.0230.019~0.0330.004
Moderate drinking///Moderate drinking0.0320.023~0.054<0.001
Smoking///Smoking0.0660.056~0.076<0.001
Age phase II: 34 ≤ age ≤ 56 (N = 59,807)
Age0.0020.001~0.003<0.001
Systolic BP///
Diastolic BP0.0080.004~0.0110.002
Glucose (mmol/L)0.0320.021~0.039<0.001
BMI (kg/m2)0.0290.019~0.0450.002
Inactive lifestyle0.0160.012~0.0230.002
Moderate drinking0.0560.043~0.077<0.001
Smoking0.1030.078~0.2010.003
Age phase II: age ≥ 57 (N = 8,748)Age phase III: age ≥ 57 (N = 15,653)
Age-0.013-0.010 ~ -0.007<0.001Age-0.008-0.010 ~ -0.007<0.001
Systolic BP///Systolic BP///
Diastolic BP0.0130.009~0.017<0.001Diastolic BP0.0040.001~0.0080.027
Glucose (mmol/L)0.0490.032~0.076<0.001Glucose (mmol/L)0.0290.021~0.0430.003
BMI (kg/m2)0.0170.012~0.032<0.001BMI (kg/m2)0.0190.011~0.044<0.001
Inactive lifestyle0.0180.012~0.0330.003Inactive lifestyle0.0230.014~0.033<0.001
Moderate drinking///Moderate drinking0.0210.014~0.034<0.001
Smoking///Smoking0.0180.016~0.0340.003
Table 2  Associations with LDL-C level evaluated by univariate linear regression analyses.
FemaleMale
VariableCoef.95% CIPVariableCoef.95% CIP
Pooled
Age0.0260.026~0.027<0.001Age0.0050.004~0.005<0.001
Systolic BP///Systolic BP///
Diastolic BP0.0210.017~0.0330.003Diastolic BP0.0050.003~0.0170.005
Glucose (mmol/L)0.0150.011~0.0210.004Glucose (mmol/L)0.2110.139~0.3410.004
BMI (kg/m2)0.0540.039~0.061<0.001BMI (kg/m2)0.0790.067~0.087<0.001
Inactive lifestyle0.0310.022~0.0410.003Inactive lifestyle0.0540.034~0.0690.003
Moderate drinking///Moderate drinking0.0490.041~0.051<0.001
Smoking///Smoking0.0680.056~0.0990.008
Age-stratified
Age phase I: 18 ≤ age ≤ 56 (N = 61,420)Age phase I: 18 ≤ age ≤ 33 (N = 22,942)
Age0.0330.033~0.034<0.001Age0.0520.049~0.055 <0.001
Systolic BP///Systolic BP0.0050.002~0.0230.027
Diastolic BP0.0290.021~0.031<0.001Diastolic BP///
Glucose (mmol/L)0.2010.178~0.231<0.001Glucose (mmol/L)0.3120.201~0.4020.004
BMI (kg/m2)0.0580.048~0.068<0.001BMI (kg/m2)0.0790.056~0.087<0.001
Inactive lifestyle0.0280.021~0.039<0.001Inactive lifestyle0.0360.026~0.0570.004
Moderate drinking///Moderate drinking0.0340.023~0.045<0.001
Smoking///Smoking0.0790.065~0.1010.008
Age phase II: 34 ≤ age ≤ 56 (N = 59,807)
Age0.0050.004~0.006<0.001
Systolic BP///
Diastolic BP0.0070.005~0.0110.006
Glucose (mmol/L)0.1920.136~0.203<0.001
BMI (kg/m2)0.0610.053~0.072<0.001
Inactive lifestyle0.1010.079~0.2090.002
Moderate drinking0.0630.045~0.0760.008
Smoking0.1140.094~0.2030.009
Age phase II: age ≥ 57 (N = 8,748)Age phase III: age ≥ 57 (N = 15,653)
Age-0.014-0.017~-0.011<0.001Age-0.017-0.019~-0.015<0.001
Systolic BP///Systolic BP///
Diastolic BP0.0090.005~0.0130.004Diastolic BP0.0060.003~0.0230.034
Glucose (mmol/L)0.0650.054~0.1050.003Glucose (mmol/L)0.1140.098~0.2310.006
BMI (kg/m2)0.0410.038~0.059<0.001BMI (kg/m2)0.0420.037~0.053<0.001
Inactive lifestyle0.0310.022~0.044<0.001Inactive lifestyle0.0410.029~0.0570.005
Moderate drinking///Moderate drinking0.0410.036~0.0550.003
Smoking///Smoking0.0310.019~0.0650.007
Table 3  Associations with non-HDL-C level evaluated by univariate linear regression analyses.
FemaleMale
Independent variableCoef.95% CIPIndependent variableCoef.95% CIP
Age phase I: 18 ≤ age ≤ 56 Age phase I: 18 ≤ age ≤ 33
Age0.0180.018~0.019<0.001Age0.0250.021~0.030<0.001
Systolic BP///Systolic BP///
Diastolic BP0.0040.002~0.007<0.001Diastolic BP///
Glucose (mmol/L)0.1150.105~0.125<0.001Glucose (mmol/L)0.1690.153~0.186<0.001
BMI (kg/m2)0.0410.032~0.050<0.001BMI (kg/m2)0.0710.055~0.087<0.001
Inactive lifestyle0.0430.032~0.089 <0.001Inactive lifestyle0.0210.011~0.067<0.001
Moderate drinking///Moderate drinking0.0310.012~0.071<0.001
Smoking///Smoking0.0750.061~0.0790.002
Age phase II: 34 ≤ age ≤ 56
Age///
Systolic BP///
Diastolic BP0.0070.003~0.010<0.001
Glucose (mmol/L)0.0300.022~0.037 <0.001
BMI (kg/m2)0.0350.023~0.048<0.001
Inactive lifestyle0.0230.013~0.0370.004
Moderate drinking0.0610.054~0.2010.003
Smoking0.1030.108~0.2320.001
Age phase II: age ≥ 57Age phase III: age ≥ 57
Age-0.013-0.016~-0.0110.001Age-0.008-0.010~-0.005<0.001
Systolic BP///Systolic BP///
Diastolic BP0.0120.007~0.018<0.001Diastolic BP0.0040.001~0.0070.002
Glucose (mmol/L)0.0510.019~0.083)<0.001Glucose (mmol/L)0.0380.026~0.050<0.001
BMI (kg/m2)0.0240.010~0.0380.002BMI (kg/m2)0.0250.010~0.040<0.001
Inactive lifestyle0.0210.016~0.0370.007Inactive lifestyle0.0340.020~0.0430.004
Moderate drinking///Moderate drinking0.0430.032~0.0770.009
Smoking///Smoking0.0230.016~0.0520.008
Table 4  Age associations with the LDL-C levels in each age phase adjusted for confounding factors.
FemaleMale
Independent variableCoef.95% CIPIndependent variableCoef.95% CIP
Age phase I: 18 ≤ age ≤ 56Age phase I: 18 ≤ age ≤ 33
Age0.0310.029~0.032<0.001Age0.0530.049~0.056<0.001
Systolic BP///Systolic BP0.0040.001~0.008<0.001
Diastolic BP0.0300.023~0.039<0.001Diastolic BP///
Glucose (mmol/L)0.1910.179~0.202<0.001Glucose (mmol/L)0.3190.299~0.339<0.001
BMI (kg/m2)0.0560.044~0.064<0.001BMI (kg/m2)0.0850.067~0.103<0.001
Inactive lifestyle0.0290.019~0.045 <0.001Inactive lifestyle0.0340.027~0.0430.008
Moderate drinking///Moderate drinking0.0370.023~0.058<0.001
Smoking///Smoking0.1110.091~0.1750.006
Age phase II: 34 ≤ age ≤ 56
Age///
Systolic BP///
Diastolic BP0.0080.004~0.011<0.001
Glucose (mmol/L)0.1810.172~0.188<0.001
BMI (kg/m2)0.0640.049~0.078<0.001
Inactive lifestyle0.0870.081~0.092<0.001
Moderate drinking0.0310.029~0.0430.009
Smoking0.1180.111~0.132<0.001
Age phase II: age ≥ 57Age phase III: age ≥ 57
Age-0.015-0.017~-0.012<0.001Age-0.018-0.020~-0.016<0.001
Systolic BP///Systolic BP///
Diastolic BP0.0120.006~0.018<0.001Diastolic BP0.0050.001~0.0060.004
Glucose (mmol/L)0.0730.036~0.110<0.001Glucose (mmol/L)0.1170.103~0.131<0.001
BMI (kg/m2)0.0390.023~0.055<0.001BMI (kg/m2)0.0430.027~0.060<0.001
Inactive lifestyle0.0280.017~0.0330.007Inactive lifestyle0.0320.021~0.0680.008
Moderate drinking///Moderate drinking0.0210.018~0.0340.001
Smoking///Smoking0.0190.018~0.0340.007
Table 5  Age associations with the non-HDL-C level in each age phase adjusted for confounding factors.
[1] Jacobson TA, Ito MK, Maki KC, Orringer CE, Bays HE, Jones PH, et al. (2015). National lipid association recommendations for patient-centered management of dyslipidemia: part 1--full report. J Clin Lipidol, 9:129-69.
[2] Stamler J, Wentworth D, Neaton JD. (1986). Is the relationship between serum cholesterol and risk of premature death from coronary heart disease continuous and graded? Findings in 356,222 primary screen-ees of the Multiple Risk Factor Intervention Trial (MRFIT). JAMA, 256:2823-8.
[3] Anderson KM, Castelli WP, Levy D. (1987). Cholesterol and mortality: 30 years of follow-up from the Framingham Study. JAMA, 257: 2176-80.
[4] Law MR, Wald NJ, Thompson SG. (1994). By how much and how quickly does reduction in serum cholesterol concentration lower risk of ischaemic heart disease? BMJ, 308:367-72.
[5] Nordestgaard BG. (2016). Triglyceride-Rich Lipoproteins and Atherosclerotic Cardiovascular Disease: New Insights From Epidemiology, Genetics, and Biology. Circ Res, 118:547-63.
[6] Goff DC Jr, Lloyd-Jones DM, Bennett G, Coady S, D'Agostino RB, Gibbons R, et al. (2014). 2013 ACC/AHA guideline on the assessment of cardiovascular risk: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. Circulation, 129:S49-73.
[7] Stone NJ, Robinson JG, Lichtenstein AH, Bairey Merz CN, Blum CB, Eckel RH, et al. (2014). 2013 ACC/AHA guideline on the treatment of blood cholesterol to reduce atherosclerotic cardiovascular risk in adults: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. J Am Coll Cardiol, 63:2889-934.
[8] Cooney MT, Dudina A, D'Agostino R, Graham IM. (2010). Cardiovascular risk-estimation systems in primary prevention: do they differ? Do they make a difference? Can we see the future? Circulation,122:300-10.
[9] Lloyd-Jones DM. (2010) Cardiovascular risk prediction: basic concepts, current status, and future directions. Circulation,121:1768-77.
[10] Payne RA. (2012). Cardiovascular risk. Br J Clin Pharmacol,74:396-410.
[11] Chapman MJ, Ginsberg HN, Amarenco P, Andreotti F, Borén J, Catapano AL, et al. (2011). Triglyceride-rich lipoproteins and high-density lipoprotein cholesterol in patients at high risk of cardiovascular disease: evidence and guidance for management. Eur Heart J, 32:1345-61.
[12] National Center for Health Statistics-National Heart, Lung,Blood Institute Collaborative Lipid Group. (1987). Trends in serum cholesterol levels among US adults aged 20 to 74 years. Data from the National Health and Nutrition Examination Surveys, 1960 to 1980. JAMA,257:937-42.
[13] Carroll MD, Kit BK, Lacher DA, Shero ST, Mussolino ME. (2012) Trends in lipids and lipoproteins in US adults, 1988-2010. JAMA,308:1545-54.
[14] Rosinger A, Carroll MD, Lacher D, Ogden C. (2017). Trends in Total Cholesterol, Triglycerides, and Low-Density Lipoprotein in US Adults, 1999-2014. JAMA Cardiol, 2:339-41.
[15] Benjamin EJ, Blaha MJ, Chiuve SE, Cushman M, Das SR, Deo R, et al. (2017). Heart Disease and Stroke Statistics-2017 Update: A Report From the American Heart Association. Circulation,135:e146-e603.
[16] 16 Johnson SC, Rabinovitch PS, Kaeberlein M. (2013). mTOR is a key modulator of ageing and age-related disease. Nature, 493:338-45.
[17] Mc Auley MT, Mooney KM. (2015). Computationally Modeling Lipid Metabolism and Aging: A Mini-review.Comput Struct Biotechnol J,13: 38-46.
[18] U.S. Department of Health and Human Services and U.S. Department of Agriculture.2015-2020 Dietary Guidelines for Americans. https://health.gov/dietaryguidelines/2015/guidelines/. (24 December 2018)
[19] Baecke JA, Burema J, Frijters JE. (1982). A short questionnaire for the measurement of habitual physical activity in epidemiological studies. Am J Clin Nutr, 36:936-42.
[20] Hu W, Jin X, Chen C, Zhang P, Li D, Su Q, et al. (2017). Diastolic Blood Pressure Rises with the Exacerbation of Obstructive Sleep Apnea in Males. Obesity (Silver Spring), 25:1980-7.
[21] Carroll MD, Lacher DA, Sorlie PD, Cleeman JI, Gordon DJ, Wolz M, et al. (2005). Trends in serum lipids and lipoproteins of adults, 1960-2002. JAMA, 294:1773-81.
[22] Ettinger WH, Wahl PW, Kuller LH, Bush TL, Tracy RP, Manolio TA, et al. (1992). Lipoprotein lipids in older people. Results from the Cardiovascular Health Study. The CHS Collaborative Research Group. Circulation, 86:858-69.
[23] Swiger KJ, Martin SS, Blaha MJ, Toth PP, Nasir K, Michos ED, et al. (2014). Narrowing sex differences in lipoprotein cholesterol subclasses following mid-life: the very large database of lipids (VLDL-10B). J Am Heart Assoc, 3:e000851.
[24] Park YM, Sui X, Liu J, Zhou H, Kokkinos PF, Lavie CJ, et al. (2015). The effect of cardiorespiratory fitness on age-related lipids and lipoproteins. J Am Coll Cardiol, 65:2091-100.
[25] Barzilai N, Huffman DM, Muzumdar RH, Bartke A. (2012). The Critical Role of Metabolic Pathways in Aging. Diabetes,61: 1315-22.
[26] Clemmons DR. (2004). The relative roles of growth hormone and IGF-1 in controlling insulin sensitivity. J Clin Invest,113:25-7.
[27] Schubert CM, Rogers NL, Remsberg KE, Sun SS, Chumlea WC, Demerath EW, et al. (2006). Lipids, lipoproteins, lifestyle, adiposity and fat-free mass during middle age: the Fels Longitudinal Study. Int J Obes (Lond),30:251-60.
[28] Garry PJ, Hunt WC, Koehler KM, VanderJagt DJ, Vellas BJ. (1992). Longitudinal study of dietary intakes and plasma lipids in healthy elderly men and women. Am J Clin Nutr, 55:682-8.
[29] Neville MM, Geppert J, Min Y, Grimble G, Crawford MA, Ghebremeskel K. (2012). Dietary fat intake, body composition and blood lipids of university men and women. Nutr Health,21:173-85.
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