Study design and participants
Participants in the biomarker substudy from the 6th (2011) wave of the CLHLS were recruited in this secondary analysis. The CLHLS is the first and largest nationwide, community-based, longitudinal prospective cohort survey concerning older adults in China(38). It provides information on the health status, socio-economic characteristics, and lifestyles of elderly individuals, including a large percentage of the oldest population(37). The in-depth study was launched in eight “longevity areas” of China (Laizhou of Shandong Province, Xiayi of Henan Province, Zhongxiang of Hubei Province, Mayang of Hunan Province, Sanshui of Guangdong Province, Yongfu of Guangxi Autonomous Region, Chengmai of Hainan Province, Rudong of Jiangsu Province), where the density of centenarians was exceptionally high and the environmental quality was very good, as evaluated and officially designated by the expert committee of the Chinese Gerontology Association(36).
During the in-depth study, the Chinese Center for Disease Control and Prevention (CDC) local network medical doctors conducted physical examinations of the participants and collected biomarker datasets containing approximately 30 indicators in routine blood tests, blood biochemical tests, and urine tests(36). More detailed descriptions have been previously published elsewhere(39-41).
Initially, a total of 2439 elderly participants were included in the study. We excluded those of younger age (less than 80, n=834, 34.2%) and those with missing data on SOF index components (n=281, 11.5%). Finally, we retained 1324 older adults in this study.
Outcome
Consistent with previous studies of secondary analyses involving CLHLS data(42), frailty was defined by the SOF index in the current study. Three components were included in the index: underweight (defined as body mass index <18.5), low energy level (indicated by a positive response to the question “Over the last 6 months, have you been limited in activities because of a health problem?”), and muscle strength (inability to stand up from a chair without the assistance of arms). As suggested, participants with two or more of the three components were defined as frail.
Exposure
Fasting venous blood was collected after an overnight fast from all willing participants. Procedures for the collection and shipment of blood samples were described in detail elsewhere(14). 25(OH)D was assayed by an enzyme-linked immunoassay using Immunodiagnostic Systems Limited (IDS Ltd, Boldon, UK). The 25(OH)D level was expressed as nmol/L.
Covariates
We adjusted for socio-demographic variables, health characteristics and confounding biomarkers in the models. Socio-demographic variables included age, sex (female/male), marital status (married/other), residence (rural/other), education level (no schooling/≥1 year of schooling), and co-residence [with family member(s)/other].
Health characteristics included lifestyles and chronic diseases. Lifestyles consisted of smoking (yes/no), drinking (yes/no), and regular exercise (yes/no) at present. Chronic diseases included hypertension (yes/no), diabetes mellitus (yes/no), heart diseases (yes/no), cerebrovascular diseases (yes/no), and respiratory diseases (yes/no). Hypertension was defined as systolic blood pressure≥140 mmHg and/or diastolic blood pressure≥90 mmHg(43). Diabetes mellitus was diagnosed by fasting plasma glucose≥7.0 mmol/L(14, 44). Other diseases were identified by self-report.
Confounding biomarkers were 11 indicators on routine blood tests and blood biochemistry tests (36). According to previous relevant studies(19), these 11 indicators, which were largely investigated in relation to frailty, were analysed in this study: 1) inflammatory marker: C reactive protein (CRP); 2) immune marker: counts of leukocytes (WBC); 3) clinical markers: plasma albumin (ALB), total cholesterol (CHO), serum creatinine (CREA), high-density lipoprotein cholesterol (HDLC), low-density lipoprotein cholesterol (LDLC), triglyceride (TG), and haemoglobin (HGB); and 4) oxidative stress markers: malondialdehyde (MDA) and superoxide dismutase (SOD). All standard laboratory techniques were performed by the central clinical laboratory at Capital Medical University in Beijing.
Overall, few data points for most confounding variables were missing (1.05%). For the missing values, we performed multiple imputations by chained equations to increase the predictive power(45). The distributions of the observed data and imputed data are described in Supplementary Table S1 (see Additional file 1). For all the covariates, the distributions of observed and imputed values were similar.
Statistical analysis
Categorical variables were expressed as numbers and percentages, and continuous data were described as the mean (standard deviation, SD) or median (interquartile range, IQR). Characteristics among groups were compared by ANOVA, Kruskal–Wallis test or χ2 test. The IQR of the 25(OH)D level was used to divide the data into four categories. The cutoff points were 26.13, 35.89, and 50.00 nmol/L.
We used multilayer logistic regression models based on the likelihood ratio test (LRT) to determine the association between 25(OH)D level and the risk of frailty. The Box-Tidwell method was used to test the linearity between logit P and all continuous variables(46). Therefore, we used continuous terms for all the confounding biomarkers and categorized age as subgroups with 80-89, 90-99, and ≥100 years. Data are reported as odds ratios (ORs) and 95% confidence intervals (CIs) in both unadjusted and adjusted logistic regression models. A p-value of the Hosmer-Lemeshow test >0.05 indicated reasonable goodness of fit(47).
Different from previous studies, to examine the linear trend across levels of 25(OH)D, we further performed spline smoothing analysis and threshold effect analysis in the current study, which were relatively novel in studies examining the respondents’ dose-response relationship between 25(OH)D and frailty. Instead of a priori assumptions, spline smoothing analysis is a form of mixed modelling based on the generalized additive model (GAM)(48), whereby a set of associated items, for example, 25(OH)D and frailty, can visually demonstrate the linear or curvilinear relationship by figures. The threshold effect analysis, which is based on the piece-wise regression model(49), can further examine whether this relationship is segmental.
Subgroup analyses and their interactions were tested to explore whether sex and age subgroups would confound the association between 25(OH)D level and frailly. Sensitivity analysis was performed in participants with complete variables and multiple imputations separately.
A two-tailed p-value <0.05 was considered statistically significant in all analyses. Statistical analyses were conducted by IBM SPSS Statistics Version 22.0, except that the spline smoothing analysis, threshold effect analysis, and multiple imputations were performed by R software Version 3.4.3 (http://www.R-project.org) and Empower® (www.empowerstats.com).
Table 1. Participant characteristics.
|
|
Variables
|
All participants (n=1324)
|
Categories (nmol/L)
|
Statistics a
|
Q1 (≤26.13)
|
Q2 (26.13–35.89)
|
Q3 (35.89–50.00)
|
Q4 (>50.00)
|
Socio-demographics, n (%)
|
|
|
|
|
|
|
Age (80-112), M (SD)
|
92.89 (7.92)
|
95.63 (7.49)
|
93.37 (7.72)
|
91.85 (7.85)
|
90.70 (7.78)
|
25.207***
|
Female
|
844 (63.7)
|
251 (75.8)
|
236 (71.3)
|
200 (60.2)
|
157 (47.6)
|
68.190***
|
Married
|
294 (22.3)
|
44 (13.3)
|
60 (18.2)
|
83 (25.1)
|
107 (32.7)
|
40.503***
|
Rural
|
1124 (84.9)
|
282 (85.2)
|
282 (85.2)
|
265 (79.8)
|
295 (89.4)
|
11.925**
|
No schooling
|
998 (76.4)
|
279 (85.8)
|
257 (78.4)
|
242 (74.2)
|
220 (67.1)
|
33.410***
|
With household member(s)
|
950 (73.2)
|
263 (82.2)
|
236 (72.2)
|
225 (69.7)
|
226 (69.1)
|
25.873***
|
Health characteristics, n (%)
|
|
|
|
|
|
|
Smoking
|
148 (11.3)
|
28 (8.5)
|
39 (11.8)
|
37 (11.2)
|
44 (13.5)
|
4.337
|
Drinking
|
167 (12.7)
|
32 (9.7)
|
41 (12.5)
|
45 (13.6)
|
49 (14.9)
|
0.218
|
Regular exercise
|
178 (13.9)
|
27 (8.4)
|
38 (11.9)
|
54 (16.7)
|
59 (18.6)
|
17.317***
|
Hypertension
|
785 (62.2)
|
197 (61.9)
|
195 (62.1)
|
192 (61.5)
|
201 (63.0)
|
0.155
|
Diabetes mellitus
|
98 (7.4)
|
28 (8.5)
|
25 (7.6)
|
24 (7.3)
|
21 (6.4)
|
1.057
|
Heart diseases
|
91 (7.0)
|
24 (7.4)
|
24 (7.4)
|
27 (8.4)
|
16 (4.9)
|
3.398
|
Cerebrovascular diseases
|
102 (7.8)
|
34 (10.4)
|
31 (9.5)
|
18 (5.5)
|
19 (5.8)
|
8.630*
|
Respiratory diseases
|
116 (8.9)
|
29 (9.0)
|
32 (9.8)
|
23 (7.0)
|
32 (9.8)
|
2.080
|
Biomarkers, M (IQR)
|
|
|
|
|
|
|
CRP (mg/L)
|
1.01 (0.41,2.93)
|
1.12 (0.38,3.35)
|
0.93 (0.43,3.05)
|
0.96 (0.41,2.54)
|
1.09 (0.39,2.75)
|
1.491
|
ALB (g/L)
|
39.10 (35.90,42.40)
|
37.90 (35.30,41.40)
|
38.60 (35.48,42.12)
|
39.70 (36.70,42.93)
|
39.90 (37.20,42.80)
|
29.923***
|
CHO (mmol/L)
|
4.16 (3.52,4.79)
|
4.03 (3.49,4.72)
|
4.21 (3.51,4.79)
|
4.21 (3.47,4.97)
|
4.20 (3.70,4.78)
|
4.186
|
CREA (mmol/L)
|
78 (65,96)
|
69 (60,85)
|
77 (63,93)
|
82 (69,98)
|
87 (71,102)
|
76.765***
|
HDLC (mmol/L)
|
1.23 (1.03,1.49)
|
1.20 (1.01,1.45)
|
1.25 (1.04,1.51)
|
1.27 (1.03,1.55)
|
1.23 (1.04,1.46)
|
5.065
|
LDLC (mmol/L)
|
2.45 (1.94,3.02)
|
2.40 (1.92,2.97)
|
2.42 (1.89,3.05)
|
2.41 (1.86,3.08)
|
2.54 (2.04,3.00)
|
5.147
|
TG (mmol/L)
|
0.79 (0.59,1.10)
|
0.78 (0.59,1.07)
|
0.79 (0.58,1.09)
|
0.82 (0.61,1.16)
|
0.77 (0.57,1.07)
|
6.429
|
SOD (IU/mL)
|
58.53 (53.43,63.24)
|
56.75 (51.75,62.97)
|
58.18 (53.49,63.20)
|
58.75 (53.33,63.06)
|
59.39 (55.39,64.24)
|
18.975***
|
MDA (μmol/L)
|
4.71 (3.73,5.79)
|
4.81 (3.93,5.89)
|
4.87 (3.88,5.91)
|
4.84 (3.82,5.83)
|
4.33 (3.25,5.55)
|
27.303***
|
WBC (109/L)
|
5.30 (4.30,6.40)
|
4.80 (4.00,6.00)
|
5.10 (4.10,6.10)
|
5.60 (4.57,6.60)
|
5.60 (5.60,6.80)
|
34.983***
|
HGB (g/L)
|
118 (106,131)
|
121 (110,133)
|
120 (107,132)
|
116 (105,129)
|
117 (105,131)
|
11.618***
|
Frailty, n (%)
|
426 (33.2)
|
162 (48.9)
|
112 (33.8)
|
93 (28.0)
|
59 (17.9)
|
76.606***
|
M (SD), mean (standard deviation); M (IQR), median (interquartile range).
a Coefficient of ANOVA, Kruskal–Wallis test or χ2 test among categories of plasma 25(OH)D level.
* <0.05, **<0.01, *** <0.001.
Abbreviations: CRP, C reactive protein; ALB, plasma albumin; CHO, total cholesterol; CREA, plasma creatine; HDLC, high-density lipoprotein cholesterol; LDLC, low-density lipoprotein cholesterol; SOD, superoxide dismutase; TG, triglyceride; SOD, superoxide dismutase; MDA, malondialdehyde; WBC, white blood cell count; HGB, haemoglobin.
|