Abstract
Objectives
With the acceleration of the process of aging population, to enjoy a higher health-related quality of life (HRQoL) is the goal of the elderly population and public health. Studies on relationship between HRQoL and lipid profile through a large sample of representative elderly population are scare.
Objective
This study was conducted to firstly explore the relationships of lipid profile with HRQoL in Chinese centenarian population.
Participants
A complete sample of 1002 participants aged over 100 years from Hainan province were recruited in the current study.
Main measure
Questionnaire investigation, physical examination and blood specimen collection were carried out by family survey. The EuroQol-5 Dimensions(EQ-5D, and EQ-VAS were used to assess HRQoL.
Results
In multivariate linear regression analyses, a significant association was found between EQ-5D and lipid profile, including total cholesterol (TC), triglyceride (TG), low density lipoprotein cholesterol (LDL-C), high density lipoprotein cholesterol (HDL-C), and similar association was also existed between EQ-VAS and TC, LDL-C HDL-C, after adjustment. The score of EQ-5D and EQ-VAS in male centenarian was higher than that of the female centenarian.
Conclusion
Lipid profile was positively associated with the HRQoL in Chinese centenarians.
Key words: Health-related quality of life, lipid profile, centenarian, association
Introduction
Healthy aging focuses on health issues of the elderly caused by the global aging trend, including disease burden and quality of life. The longer people live, the higher life quality they want to live (1). HRQoL is increasingly recognized as a representative indicator to measure the impact of diseases on health outcomes, such as the health burden of frailty (2) and chronic diseases (3). HRQoL is defined as an individual's daily functional status and perceived well-being in the fields of physical, mental and social health (4), which is an independent predictor of all-cause mortality in the elderly (5).
Hyperlipidemia is a key risk factor and main cause of cardiovascular disease (6). However, in the oldest-old, even the centenarian population, elevated lipid profile may not be a risk factor for health and longevity (7, 8), but a protective factor, which is not consistent with the traditional cognition of the health effects of blood lipid. The elderly with higher levels of lipid profile having better activities of daily living (9, 10), cognitive function (11, 12) and quality of life have been reported elsewhere. However, few studies are focused on the relationship between HRQoL and lipid profile in the centenarian population known as the most representative population of longevity. The aim of the current study is to estimate the possible relationship between lipid profile and HRQoL in China Hainan Centenarians Cohort Study (CHCCS), a complete sample of centenarians and largest samples of centenarians in Asia.
Methods
Study population
This cross-sectional data, from June 2014 to December 2016, were from CHCCS, designed as a complete centenarian sample and a community-based study in Hainan province, China. The protocol of CHCCS have been reported elsewhere (13). A total of 1002 centenarians, including 180 men and 822 women, were recruited as the subjects and interviewed by family survey including questionnaire, physical examination and blood specimen collection. The CHCCS was approved by the Ethics Committee of the Hainan Branch of the Chinese People's Liberation Army General Hospital (approval number, 301hn11201601). The study was conducted in accordance with the Helsinki declaration and all centenarians had signed the written constant agreement.
Measurement
Lipid profile
Automatic biochemical analyzer (Cobas c702; Roche Products Ltd, Basel, Switzerland) was used to estimate the lipid profile, including TC, TG, HDL-C and LDL-C, by enzymatic analyses (Roche Products Ltd, Basel, Switzerland). All the blood specimens were analyzed by technicians from the center laboratory of Hainan Branch of PLA General Hospital, who did not know the source and function of blood samples. Lipid profile indicators were divided into categorical variable of tertiles for better analysis, respectively.
HRQoL
The EQ-5D is used to measure HRQoL in the elderly, which covers five dimensions, including mobility, self-care, regular activities, pain/discomfort and anxiety/depression (14). EQ-VAS is a 20cm visualized analogue scale, which can quantify the subjective health status of subjects ranging from 0 (Self perceived worst health) to 100 (Self perceived best health). The field survey of EQ-5D and EQ-VAS was conducted by trained investigator. The EQ-5D of the time tradeoff (TTO) model in Japanese was used in this study, which has been previously reported as the most suitable index for Chinese population (15).
Covariables
Information regarding each individual’s age, sex, body mass index (BMI); systolic blood pressure (SBP); diastolic blood pressure (DBP); fasting plasma glucose (FPG); nationality; educational level; marital status; resident style was collected and measured by trained multidisciplinary medical team from Hainan Branch of PLA General Hospital. The systolic and diastolic pressures were measured twice on the right arm by electronic sphygmomanometer when the centenarian was in the state of quietness. The average of the two measurements was used for the final analysis. Fasting blood samples were collected by experienced nurses, shaken and placed in a bio-refrigeration box, and then transported to the Laboratory of Hainan Branch of the Chinese PLA General Hospital for detecting lipid profile, FPG and serum uric acid within 6 hours. All analyses were conducted by clinical laboratory technicians who were blinded to the clinical design and data. BMI was classified into three categories, including underweight (<18.5 kg/m2), normal body weight (18.5–24.0 kg/m2), and overweight (>24.0 kg/m2) (16).
Statistical analysis
EmpowerStats (http://www.empowerstats.com, X&Y Solutions, Inc., Boston, MA) and statistical software packages R (http://www.R-project.org, The R Foundation) were used to perform all the analyses. Lilliefors test was used to estimate the normality of continuous variables. In this study, the difference of continuous variables subjecting to the normal distribution were tested by one-way ANOVA and the continuous variables not subjecting to the normal distribution by Kruskal-Wallis test, and the results were represented by mean± standard deviation and median [interquartile range (IQR)], respectively. The difference of categorical variable was estimated by Chi-square test and the results were represented by number and percentage(n/%). Univariate and multivariate linear regression models were used to evaluate the associations between lipid profile and HRQoL. Besides, generalized additive model (GAM) was used to exactly visualize the dose-response relationship between lipid profile and HRQoL. The significance level of all tests was set at a two tailed a value of 0.05.
Results
Median age of all centenarians was 102 years. The average scores of EQ-5D index and EQ-VAS were 0.62 and 61.60, respectively. Lipid profile levels were described in Table 1. Univariate analysis for EQ-5D index and VAS score were described in Table 2, respectively. Centenarians with higher levels of lipid profile, including TC, TG, LDL-C and HDL-C, had higher EQ-5D index (Table 3), and the similar relationship was found between the levels of TC or LDL-C and EQ-VAS in centenarians (Table 4). Lipid profile indicators were positively associated with the HRQoL among Hainan centenarians. The male centenarians had higher scores of EQ-5D index and VAS, while the stronger relationship between lipid profile and EQ-5D index was found in female centenarians. (Fig. 1, 2)
Table 1.
Baseline characteristics of total cholesterol in participants (N =1002)
| Variables | All | TC Tertiles (mmol/L) | P-value | ||
|---|---|---|---|---|---|
| T1 (≤4.26) | T2 (4.27–4.96) | T3 (≥4.97) | |||
| Median (IQR) | |||||
| No. of participants | 1002 | 336 | 336 | 330 | |
| Age(year) | 102.00 (101.00–104.00) | 102.00 (101.00–104.00) | 102.00 (101.00–104.00) | 102.00 (101.00–104.00) | 0.732 |
| SBP (mmHg) | 152.00 (137.00–167.00) | 149.50 (132.00–163.25) | 151.00 (136.00–167.00) | 152.00 (142.00–169.00) | 0.002 |
| TG (mmol/L) | 1.05 (0.80–1.38) | 0.88 (0.68–1.15) | 1.09 (0.84–1.34) | 1.21 (0.88–1.60) | <0.001 |
| LDL-C (mmol/L) | 2.77 (2.30–3.24) | 2.12 (1.81–2.39) | 2.79 (2.50–2.92) | 3.49 (3.17–3.96) | <0.001 |
| HDL-C (mmol/L) | 1.41 (1.18–1.65) | 1.26 (1.03–1.47) | 1.44 (1.25–1.65) | 1.53 (1.28–1.86) | <0.001 |
| FPG (mmol/L) | 4.97 (4.26–5.64) | 4.97 (4.19–5.88) | 5.01 (4.27–5.47) | 4.88 (4.29–5.64) | 0.887 |
| Uric acid (mmol/L) | 317.64 (264.00–383.75) | 320.00 (255.75–387.25) | 317.64 (276.00–381.38) | 313.50 (262.00–382.75) | 0.488 |
| Mean±SD | |||||
| DBP (mmHg) | 75.32 ± 11.89 | 73.69 ± 12.15 | 74.91 ± 11.36 | 77.35 ± 11.88 | <0.001 |
| EQ-5D index | 0.62 ± 0.25 | 0.58 ± 0.26 | 0.61 ± 0.25 | 0.66 ± 0.23 | 0.001 |
| VAS score | 61.60 ± 15.56 | 60.09 ± 15.87 | 61.99 ± 16.11 | 62.71 ± 14.60 | 0.081 |
| N (%) | |||||
| Gender | <0.001 | ||||
| Male | 180 (17.96%) | 86 (25.60%) | 49 (14.58%) | 45 (13.64%) | |
| Female | 822 (82.04%) | 250 (74.40%) | 287 (85.42%) | 285 (86.36%) | |
| BMI (kg/m2) | 0.042 | ||||
| <18.5 | 575 (57.39%) | 205 (61.01%) | 191 (56.85%) | 179 (54.24%) | |
| 18.5–24.0 | 390 (38.92%) | 126 (37.50%) | 132 (39.29%) | 132 (40.00%) | |
| >24.0 | 37 (3.69%) | 5 (1.49%) | 13 (3.87%) | 19 (5.76%) | |
| Nationality | 0.182 | ||||
| Han | 883 (88.12%) | 289 (86.01%) | 295 (87.80%) | 299 (90.61%) | |
| Other nation | 119 (11.88%) | 47 (13.99%) | 41 (12.20%) | 31 (9.39%) | |
| Educational levels | 0.723 | ||||
| Illiteracy | 915 (91.32%) | 306 (91.07%) | 304 (90.48%) | 305 (92.42%) | |
| Primary school and above | 87 (8.68%) | 30 (8.93%) | 32 (9.53%) | 25 (7.58%) | |
| Marital status | 0.740 | ||||
| Married | 100 (9.98%) | 37 (11.01%) | 32 (9.52%) | 31 (9.39%) | |
| Widowed/divorced | 902 (90.02%) | 299 (88.99%) | 304 (90.48%) | 299 (90.61%) | |
| Resident style | 0.777 | ||||
| Living with family | 863 (86.13%) | 293 (87.20%) | 287 (85.42%) | 283 (85.76%) | |
| Living alone/nursing home | 139(13.87%) | 43 (12.80%) | 49 (14.58%) | 47 (14.24%) | |
Abbreviations: IQR, interquartile range; SBP, Systolic blood pressure; DBP, diastolic blood pressure; BMI, body mass index; FPG, fasting plasma glucose; TC, total cholesterol; TG, triacylglycerol; LDL-C, low density lipoprotein cholesterol; HDL-C, high density lipoprotein cholesterol.
Table 2.
Univariate analysis for EQ-5D index and VAS score
| Variables | EQ-5D index | VAS | ||
|---|---|---|---|---|
| (β, 95%CI) | P-value | (β, 95%CI) | P-value | |
| Age | −0.0032 (−0.0088, 0.0025) | 0.2680 | −0.6366 (−0.9880, −0.2852) | 0.0004 |
| DBP | 0.0004 (−0.0009, 0.0017) | 0.5022 | −0.0551 (−0.1362, 0.0259) | 0.1829 |
| SBP | 0.0009 (0.0002, 0.0016) | 0.0110 | 0.0249 (−0.0176, 0.0674) | 0.2506 |
| FPG | −0.0103 (−0.0210, 0.0004) | 0.0589 | −0.8250 (−1.4934, −0.1567) | 0.0157 |
| TC | 0.0367 (0.0213, 0.0521) | <0.0001 | 1.0014 (0.0280, 1.9748) | 0.0440 |
| TG | 0.0332 (0.0095, 0.0568) | 0.0061 | 0.3392 (−1.1474, 1.8258) | 0.6548 |
| HDL-C | 0.1011 (0.0621, 0.1402) | <0.0001 | 2.3761 (−0.0982, 4.8504) | 0.0601 |
| LDL-C | 0.0433 (0.0240, 0.0627) | <0.0001 | 1.3824 (0.1615, 2.6034) | 0.0267 |
| Uric acid | 0.0003 (0.0001, 0.0004) | 0.0014 | 0.0115 (0.0017, 0.0213) | 0.0220 |
| Gender | ||||
| Male | Ref. | Ref. | ||
| Female | −0.0758 (−0.1156, −0.0359) | <0.0001 | −4.4858 (−6.9818, −1.9898) | 0.0004 |
| BMI (kg/m2) | ||||
| <18.5 | Ref. | Ref. | ||
| 18.5–24.0 | 0.0734 (0.0418, 0.1050) | <0.0001 | 2.5288 (0.5346, 4.5229) | 0.0131 |
| >24.0 | 0.0818 (0.0000, 0.1636) | 0.0502 | 5.3123 (0.1566, 10.4681) | 0.0437 |
Abbreviations: SBP, Systolic blood pressure; DBP, diastolic blood pressure; BMI, body mass index; FPG, fasting plasma glucose; TC, total cholesterol; TG, triacylglycerol; LDL-C, low density lipoprotein cholesterol; HDL-C, high density lipoprotein cholesterol.
Table 3.
Relationship between lipid profile and EQ-5D index in different models
| Crude Model | Adjusted Model | |||
|---|---|---|---|---|
| β (95%CI) Pvalue | P value | β (95%CI) Pvalue | P value | |
| TC | 0.04 (0.02, 0.05) | <0.0001 | 0.03 (0.02, 0.05) | <0.0001 |
| TC Tertile | ||||
| Low | Ref. | Ref. | ||
| Middle | 0.03 (−0.01, 0.06) | 0.1903 | 0.02 (−0.01, 0.06) | 0.2392 |
| High | 0.08 (0.04, 0.11) | <0.0001 | 0.07 (0.03, 0.11) | 0.0004 |
| P for trend | 0.001 | 0.001 | ||
| TG | 0.03 (0.01, 0.06) | 0.0061 | 0.03 (0.01, 0.05) | 0.0167 |
| TG Tertile | ||||
| Low | Ref. | Ref. | ||
| Middle | −0.03 (−0.07, 0.01) | 0.0903 | −0.03 (−0.07, 0.01) | 0.1114 |
| High | 0.04 (0.00, 0.08) | 0.0288 | 0.04 (0.00, 0.08) | 0.0489 |
| P for trend | 0.026 | 0.05 | ||
| LDLC | 0.04 (0.02, 0.06) | <0.0001 | 0.04 (0.02, 0.06) | <0.0001 |
| LDLC Tertile | ||||
| Low | Ref. | Ref. | ||
| Middle | 0.00 (−0.03, 0.04) | 0.8481 | −0.00 (−0.04, 0.04) | 0.9750 |
| High | 0.06 (0.03, 0.10) | 0.0009 | 0.06 (0.02, 0.09) | 0.0031 |
| P for trend | 0.001 | 0.004 | ||
| HDLC | 0.10 (0.06, 0.14) | <0.0001 | 0.12 (0.08, 0.16) | <0.0001 |
| HDLC Tertile | ||||
| Low | Ref. | Ref. | ||
| Middle | 0.04 (0.00, 0.08) | 0.0373 | 0.05 (0.01, 0.08) | 0.0136 |
| High | 0.08 (0.04, 0.12) | <0.0001 | 0.09 (0.06, 0.13) | <0.0001 |
| P for trend | <0.0001 | <0.0001 | ||
Adjust Model: adjust for: age; gender; BMI; SBP; DBP; FPG; nationality; educational level; marital status; resident style; Abbreviations: TC, total cholesterol; TG, triacylglycerol; LDL-C, low density lipoprotein cholesterol; HDL-C, high density lipoprotein cholesterol.
Table 4.
Relationship between lipid profile and VAS in different models
| Crude Model | Adjusted Model | |||
|---|---|---|---|---|
| β (95%CI) Pvalue | P value | β (95%CI) Pvalue | P value | |
| TC | 1.00 (0.03, 1.97) | 0.044 | 1.02 (0.05, 2.00) | 0.0405 |
| TC Tertile | ||||
| Low | Ref. | Ref. | ||
| Middle | 1.90 (−0.46, 4.26) | 0.1151 | 1.88 (−0.45, 4.21) | 0.1144 |
| High | 2.62 (0.26, 4.97) | 0.0297 | 2.81 (0.46, 5.16) | 0.0194 |
| P for trend | 0.035 | 0.024 | ||
| TG | 0.34 (−1.15, 1.83) | 0.6548 | 0.17 (−1.32, 1.66) | 0.8222 |
| TG Tertile | ||||
| Low | Ref. | Ref. | ||
| Middle | 0.08 (−2.28, 2.45) | 0.9447 | 0.47 (−1.87, 2.81) | 0.6944 |
| High | 1.24 (−1.13, 3.61) | 0.3056 | 1.22 (−1.17, 3.60) | 0.3165 |
| P for trend | 0.371 | 0.457 | ||
| LDLC | 1.38 (0.16, 2.60) | 0.0267 | 1.31 (0.09, 2.54) | 0.0363 |
| LDLC Tertile | ||||
| Low | Ref. | Ref. | ||
| Middle | 1.06 (−1.30, 3.41) | 0.3804 | 1.09 (−1.24, 3.42) | 0.3596 |
| High | 2.67 (0.31, 5.03) | 0.0271 | 2.50 (0.13, 4.88) | 0.0392 |
| P for trend | 0.027 | 0.060 | ||
| HDLC | 2.38 (−0.10, 4.85) | 0.0601 | 3.49 (1.02, 5.97) | 0.0058 |
| HDLC Tertile | ||||
| Low | Ref. | Ref. | ||
| Middle | 1.65 (−0.73, 4.02) | 0.1741 | 2.08 (−0.26, 4.42) | 0.0820 |
| High | 1.45 (−0.91, 3.81) | 0.2298 | 2.32 (−0.04, 4.67) | 0.0540 |
| P for trend | 0.234 | 0.060 | ||
Adjust Model: adjust for: age; gender; BMI; SBP; DBP; FPG; nationality; educational level; marital status; resident style; Abbreviations: TC, total cholesterol; TG, triacylglycerol; LDL-C, low density lipoprotein cholesterol; HDL-C, high density lipoprotein cholesterol.
Figure 1.
Association of lipid profile level (TC, TG LDL-C and HDL-C) and EQ-5D index in Chinese centenarians by Chart A-H, respectively, in a generalized additive model (GAM). Solid rad line represents the smooth curve fit between variables and blue bands represent the 95% of confidence interval from the fit in chart A-D. Red line represents male centenarians and green line female centenarian in chart E-H. All adjusted for age; gender; BMI; SBP; DBP; FPG; nationality; educational level; marital status; resident style
Figure 2.
Association of lipid profile level (TC, TG LDL-C and HDL-C) and VAS in Chinese centenarians by Chart A-H, respectively, in a generalized additive model (GAM). Solid rad line represents the smooth curve fit between variables and blue bands represent the 95% of confidence interval from the fit in chart A-D. Red line represents male centenarians and green line female centenarian in
Figure 1 Association of lipid profile level (TC, TG LDL-C and HDL-C) and EQ-5D index in Chinese centenarians by Chart A-H, respectively, in a generalized additive model (GAM). Solid rad line represents the smooth curve fit between variables and blue bands represent the 95% of confidence interval from the fit in chart A-D. Red line represents male centenarians and green line female centenarian in chart E-H. All adjusted for age; gender; BMI; SBP; DBP; FPG; nationality; educational level; marital status; resident style
Figure 2 Association of lipid profile level (TC, TG LDL-C and HDL-C) and VAS in Chinese centenarians by Chart A-H, respectively, in a generalized additive model (GAM). Solid rad line represents the smooth curve fit between variables and blue bands represent the 95% of confidence interval from the fit in chart A-D. Red line represents male centenarians and green line female centenarian in chart E-H. All adjusted for age; gender; BMI; SBP; DBP; FPG; nationality; educational level; marital status; resident style.
Discussion
Based on our study, we propose that elevated lipid profile levels (TC, TG, LDL-C and HDL-C) might play a key role to improve the HRQoL (EQ-5D index and VAS score) among Chinese centenarian population. Higher though the HRQoL of the male centenarian population is, the association of HRQoL and lipid profile might be stronger in female centenarian population.
To our knowledge, the study on the quality of life focusing on complete sample of the community centenarian population is scare. The average EQ-5D index from the community-dwelling seniors aged >72 years old in Germany is 81.1 (17). Another study from Vietnam showed that the average score of EQ-5D index and VAS were 0.8 and 57.5 in the elderly diabetic outpatients (18). A study focusing on community-dwelling elderly age >85 years old in the Netherlands, showed the mean EQ-5D indices and VAS were 0.86 and 76, respectively (19). The results above showed the higher scores of EQ-5D indices and VAS than that of this study, because the age group of subjects was different.
Few studies have focused on the relationship between lipid profile and HRQoL in centenarian population. Previous studies have suggested that decreases in LDL-C or increases in HDL-C were independently associated with increases in the HRQoL among middle-aged or elder people (20). A significant correlation between hyperlipidemia and lower EQ-5D was found among Chinese resident adult patient (21). The results above are not consistent with our results. The most likely reason for this diversity is that the diverse distribution characteristics of lipid profile in the octogenarian or centenarian differ from those in adults. The octogenarian or centenarian population might have overcome the early death and diseases related to dyslipidemia, and the blood lipid levels at this age group may better reflect their physiological effects, which needs further verification by other studies.
Women constitute 82% of Hainan centenarians, while men have a higher score of EQ-5D and EQ-VAS, suggesting that among centenarians, more likely, women live longer and men live better in this study. Studies have confirmed that women live longer than men across almost all ages, as the hypothesis of women's fundamental biological underpinnings (22). It is not clear why the quality of life of male centenarians is higher, and the possible reason may be the bias caused by the small sample size of male centenarians in this study, but we are more inclined to believe that only the healthiest men can live over 100, while the healthier women can easily live over 100, so, the result seem to men have a better quality of life. This indirectly proves that women can live longer.
Several limitations should be noted in this study. First, it is a cross-sectional study that cannot conclude a causal relationship between lipid profile and HRQoL among the subjects. Second, the scores of EQ-5D and EQ-VAS were self-reported and individual subjective information bias was inevitable, although the questionnaire information was confirmed by family members or caregivers. Finally, the sample size of male centenarians was far less than that of female centenarians, so the possible gender difference of correlation between the lipid profile and HRQoL needs further analysis.
Conclusion
The dose-response association was found between lipid profile and HRQoL (EQ-5D index and EQ-VAS) in Chinese centenarians. The stronger relationship was found in female centenarians and the higher HRQoL in male centenarians. A possible correlation between blood lipid and HRQoL was proposed in the current study and health effects of lipid profile might need to be re-recognized and further verification among the centenarian population.
Acknowledgments
All authors thank staff who participated in the field survey and centenarians in CHCCS. This study was financed by Beijing Nova Program (Z181100006218085), Opening Foundation of State Key Laboratory of Kidney Diseases (KF-01-115), Opening Foundation of National Clinical Research Center of Geriatrics (NCRCG-PLAGH-2017017), National Natural Science Foundation of China (81773502, 81703285, 81703308), Medical Big Data Fund of Chinese PLA General Hospital (2018MBD-029).
Contributor Information
Miao Liu, Email: liumiaolmbxb@163.com.
Y. He, Email: yhe301@x263.net.
Ethical Standards
This study has been approved by the Ethics Committee of the Hainan Branch of the Chinese People's Liberation Army General Hospital (Sanya, Hainan; Number: 301hn11201601). All the centenarians were informed about the purposes of the study and consequently have signed their “consent of the patient”. All investigations conformed to the principles outlined in the Declaration of Helsinki.
Conflict of interest
All authors have no conflicts of interest in this work.
Financial disclosure
All authors have no financial disclosures.
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