Socio-demographic and other health-related data for participants at baseline and after eight months of follow-up are presented in Table 1. Of the 19 of participants who did not complete the study, 5 did not continue due to sickness, 8 were no longer interested, and the remaining 6 could not be reached during follow-up. At baseline, the mean (SD) age was 70.8 (8.1), with 59% male. Most of the participants were married and had a University or college degree.
Older adults completing the study had a significantly higher SPPB (P=0.007), HGS (p=0.027) and lower WC (p=0.036) as compared to those lost to follow-up (n=19). All other variables of interest were not significantly different (See additional file 1).
Table 1: Socio-Demographic Characteristics and Other Health-Related Information at Baseline and 8-Months’ Follow-Up.
Variables
|
Baseline (n=100)
|
Follow-up (n=81)
|
Age, years Mean (SD)
|
70.8 (8.1)
|
70.7 (8.2)
|
Male N (%)
|
59 (59.0)
|
50 (62.0)
|
Ethnicity N (%)
|
Pakistani
|
23 (23.0)
|
15 (18.5)
|
Indian
|
7 (7.0)
|
5 (6.2)
|
Bangladeshi
|
4 (4.0)
|
2 (2.5)
|
Caribbean
|
41 (41.0)
|
38 (46.9)
|
African
|
19 (19.0)
|
15 (18.5)
|
Others
|
6 (6.0)
|
6 (7.4)
|
Marital status N (%)
|
Single
|
4 (4.0)
|
3 (3.7)
|
Married
|
66 (66.0)
|
55 (67.9)
|
Divorced
|
14 (14.0)
|
12 (14.8)
|
Widowed
|
16 (16.0)
|
11 (13.6)
|
Faith/Religion N (%)
|
No religion
|
1 (1.0)
|
1 (1.2)
|
Hindu
|
2 (2.0)
|
2 (2.5)
|
Sikh
|
7 (7.0)
|
4 (5.0)
|
Muslim
|
34 (34.0)
|
24 (29.6)
|
Christian
|
56 (56.0)
|
50 (61.7)
|
Education N (%)
|
No education
|
16 (16.0)
|
11 (13.6)
|
Primary
|
16 (16.0)
|
14 (17.3)
|
Secondary
|
21 (21.0)
|
15 (18.5)
|
College/University
|
47 (47.0)
|
41 (50.6)
|
Self-rated health N (%)
|
Excellent
|
18 (18.0)
|
16 (19.7)
|
Good
|
55 (55.0)
|
45 (55.6)
|
Fair
|
16 (16.0)
|
11 (13.6)
|
Poor
|
11 (11.0)
|
9 (11.1)
|
No. of diseases Mean (SD)
|
2.0 (1.4)
|
2.1 (1.4)
|
IMD Decile N (%)
|
1 (Most deprived)
|
33 (33.0)
|
29 (35.8)
|
2
|
19 (19.0)
|
19 (23.4)
|
3
|
22 (22.0)
|
11 (13.6)
|
4 (least deprived)
|
26 (26.0)
|
22 (27.2)
|
BMI categories* N (%)
|
Normal
|
7 (7.0)
|
9 (11.1)
|
Overweight
|
31 (31.0)
|
21 (25.9)
|
Obese
|
62 (62.0)
|
51 (63.0)
|
aNutritional status by
MNA-SF N(%)**
|
Malnourished
|
2 (2.0)
|
1 (1.2)
|
At- risk of malnutrition
|
22 (22.0)
|
29 (35.8)
|
Normal
|
76 (76.0)
|
51 (63.0)
|
aSPPB score Median (IQR) **
|
11.1 (4.0)
|
10.0 (4.0)
|
HGS Mean (SD)
|
27.6 (9.8)
|
26.5 (9.5)
|
WC Mean (SD)
|
100.3 (10.6)
|
100.8 (10.6)
|
SD= Standard deviation; IQR= Interquartile range; IMD= Index of Multiple Deprivation; BMI= Body Mass Index; MNA-SF= Mini-Nutritional Assessment-Short Form; HGS= Handgrip Strength; WC= Waist circumference *WHO guidance on BMI thresholds for Asian populations (World Health Organization, 2004) was used to categorise BMI of South Asian participants, and the standard BMI categories were used for Caribbean and African participants. ** p<0.05 ; a Wilcoxon Rank test used to examine median differences.
3.1 Energy and Macronutrient Intakes Over Time Compared with DRVs
Mean (SD) and median (IQR) values for energy and nutrient intakes are reported in Table 2. Energy intakes for both sexes at both time points were below the estimated average requirement based on the population with low physical activity levels. There was a decrease in polyunsaturated fatty acids intake for females at follow-up (P=0.04) (Table 2). The mean percentage Total Energy (%TE) from carbohydrates at baseline and follow-up for both sexes were slightly above the DRV. However, the %TE from fat at both time points for females were below the DRV (31.3% and 32.3%, respectively, vs 35%).
Table 2: Macronutrient and Energy Intake of Community-dwelling, Ethnically Diverse Older Adults at Baseline and Follow-up (N=81)
|
Baseline
|
Follow-up
|
|
Nutrient intake
|
Sex
|
Mean (SD)
|
Median (IQR)
|
Mean (SD)
|
Median (IQR)
|
RNI **
|
P-values*
|
Energy Kcal/day
|
Male
|
1884.6 (518.2)
|
1853.8 (609.6)
|
1965.0 (686.5)
|
1822 (797.8)
|
2103.3-2366.2
|
0.393
|
Female
|
1514.5 (428.3)
|
1358.5 (526.5)
|
1354.5 (498.5)
|
1243.5 (676.0)
|
1673.0-1888.1
|
0.065
|
Carbs g
|
Male
|
230.2 (61.9)
|
223.8 (60.9)
|
248.9 (72.1)
|
234.8 (83.0)
|
-
|
0.104
|
Female
|
195.4 (60.9)
|
201.1 (95.3)
|
178.2 (74.0)
|
161.8 (84.5)
|
|
0.189
|
%TE Carbs
|
Male
|
50.2 (11.6)
|
48.2 (17.7)
|
53.5 (10.8)
|
52.7 (16.4)
|
50
|
0.101
|
Female
|
51.8 (8.9)
|
50.2 (9.2)
|
52.1 (9.2)
|
52.3 (14.5)
|
|
0.883
|
Fibre g/day
|
Male
|
18.3 (7.3)
|
17.2 (10.0)
|
20.3 (10.8)
|
19.1 (10.5)
|
30
|
0.192
|
Female
|
17.1 (7.9)
|
15.0 (10.7)
|
14.1 (7.5)
|
12.8 (9.5)
|
|
0.084
|
Protein g
|
Male
|
79.1 (25.3)
|
74.2 (28.7)
|
77.6 (34.4)
|
73.9 (41.5)
|
|
0.736
|
Female
|
68.0 (23.0)
|
57.5 (36.6)
|
58.6 (23.4)
|
53.2 (28.2)
|
|
0.070
|
Protein g/kg/day
|
Male
|
1.02 (0.40)
|
0.94 (0.44)
|
0.98 (0.48)
|
0.90 (0.51)
|
1.0-1.2g/kg[73]
|
0.754
|
Female
|
0.93 (0.33)
|
0.86 (0.50)
|
0.80 (0.37)
|
0.76 (0.38)
|
|
0.183
|
%TE Protein
|
Male
|
17.0 (3.9)
|
16.8 (5.3)
|
15.8 (4.2)
|
15.3 (3.9)
|
15
|
0.127
|
Female
|
18.1 (4.4)
|
17.7 (4.8)
|
18.0 (5.9)
|
18.0 (6.7)
|
|
0.912
|
Fats g
|
Male
|
75.3 (40.8)
|
73.6 (49.2)
|
73.5 (44.7)
|
66.6 (39.5)
|
-
|
0.816
|
Female
|
53.3 (20.2)
|
53.0 (33.7)
|
48.8 (22.4)
|
41.6 (29.9)
|
|
0.319
|
%TE Fats
|
Male
|
34.3 (10.8)
|
36.3 (16.5)
|
38.1 (37.6)
|
32.4 (12.8)
|
35
|
0.453
|
Female
|
31.6 (8.6)
|
31.3 (14.4)
|
32.3 (7.0)
|
31.1 (10.3)
|
|
0.724
|
Saturated fats g
|
Male
|
24.2 (14.5)
|
22.2 (17.5)
|
28.9 (28.0)
|
23.6 (17.2)
|
-
|
0.203
|
Female
|
17.5 (8.9)
|
16.9 (12.4)
|
17.2 (10.7)
|
14.0 (8.5)
|
|
0.885
|
%TE Saturated fats
|
Male
|
11.2 (4.8)
|
11.0 (6.7)
|
11.3 (4.3)
|
11.1 (6.0)
|
11
|
0.885
|
Female
|
10.4 (4.6)
|
9.7 (4.8)
|
11.2 (4.2)
|
10.8 (6.7)
|
|
0.445
|
MUFA g
|
Male
|
24 (14.0)
|
22.8 (17.2)
|
24.8 (16.9)
|
20.6 (15.2)
|
-
|
0.742
|
Female
|
17.6 (8.3)
|
17.9 (12.1)
|
16.1 (9.2)
|
14.6 (8.8)
|
|
0.385
|
%TE MUFA
|
Male
|
11.1 (4.6)
|
11.3 (6.1)
|
10.3 (3.8)
|
10.6 (6.0)
|
12
|
0.310
|
Female
|
10.4 (3.9)
|
10.4 (4.8)
|
10.5 (36)
|
10.4 (3.6)
|
|
0.889
|
PUFA g
|
Male
|
13.9 (10.2)
|
10.5 (8.8)
|
15.4 (14.6)
|
10.9 (13.5)
|
|
0.460
|
Female
|
8.9 (4.1)
|
9.7 (7.1)
|
7.2 (3.7)
|
6.2 (3.6)
|
|
0.040
|
% TE PUFA
|
Male
|
6.3 (3.4)
|
5.7 (3.2)
|
6.7 (5.9)
|
5.5 (5.1)
|
|
0.626
|
Female
|
5.2 (2.0)
|
4.9 (3.0)
|
4.8 (1.7)
|
4.8 (2.5)
|
|
0.292
|
%TE= Percentage of Total Energy; RNI = reference nutrient intake SD = Standard deviation; IQR =interquartile Range; g= gram *P values showing significant difference in nutrient intake for the two time points; ** Department of Health (1991) and Scientific Advisory Committee on Nutrition (2012).
There were also differences in nutrient intakes by ethnicity. As shown in table 3, South Asians reported significantly higher intakes of energy (p=0.003) at both time points. Post-hoc analysis using the Turkey HSD test at baseline indicated that the mean score of energy intake of South Asian was significantly higher than that of African/Caribbean (p=0.028). The mean energy intakes of the Other ethnicity was also significantly higher than that of African/Caribbean ( p=0.015). However, there was no significant difference between mean energy intakes of South Asians and Other ethnicities (p=0.303). At follow-up, the post-hoc analysis showed a similar trend. The mean energy intakes of South Asians were significantly higher than that of African/Caribbean (p=0.002). There was no significant difference between intakes of Other ethnicity and the mean energy intakes of South Asians (p=0.571) or African/Caribbean (p=0.589).
There was also reported differences in the intakes of %TE PUFA at both time points (p<0.001). Post hoc analysis revealed that South Asians had significantly higher intakes than African/Caribbean at baseline (p <0.001) and follow-up (p <0.001). The %TE PUFA intakes of the Other ethnicity did not differ from South Asians (p=0.597) or African/Caribbean (p=0.256). Similarly, at follow-up there was no significant difference between Other ethnicity and South Asians (p=0.067) or African/ Caribbean (p=0.978).
Additionally, South Asians reported significantly higher intakes of %TE total fat at baseline (p=0.019) and follow-up (p=0.048). Post Hoc analysis revealed that South Asians %TE fats were significantly different from African/Caribbean at baseline (p=0.038) and follow-up (p=0.044). There was no significant difference between intakes of Other ethnicity and South Asians (p=0.819) or African/Caribbean (p=0.153) at baseline. At follow-up, it was a similar pattern, Other ethnicity intakes of %TE was not significantly different from South Asians (p=0.302) or African/Caribbean (p=0.987).
Table 3: Energy and Macronutrient Contribution to Energy by Ethnicity Over Time
|
Baseline (n=100)
|
Follow-up (n=81)
|
|
South Asian (n= 34)
|
African/
Caribbean (n= 60)
|
Others (n= 6)
|
*P-value
|
South Asian (n= 22)
|
African/ Caribbean (n= 53)
|
Others (n= 6)
|
*P-value
|
Energy kcal/day
|
1900.5
|
1615.8
|
1873.3
|
0.003
|
1969.8
|
1470.0
|
1641.5
|
0.003
|
%TE Carbs
|
48.6
|
49.0
|
43.9
|
0.625
|
48.3
|
55.4
|
53.0
|
0.141
|
%TE Sugars
|
12.1
|
15.1
|
17.4
|
0.119a
|
14.9
|
17.4
|
20.5
|
0.056 a
|
%TE Protein
|
16.4
|
17.7
|
13.8
|
0.013
|
15.3
|
16.4
|
13.8
|
0.444 a
|
%TE Fats
|
37.6
|
32.4
|
42.8
|
0.019
|
36.6
|
30.1
|
31.3
|
0.048
|
%TE Saturated fats
|
10.0
|
10.8
|
13.0
|
0.687
|
12.0
|
10.7
|
12.1
|
0.329
|
%TE Monounsaturated
|
11.1
|
11.3
|
13.4
|
0.385
|
10.8
|
10.4
|
11.3
|
0.768
|
%TE Polyunsaturated
|
7.2
|
4.9
|
7.0
|
<0.001
|
8.1
|
4.3
|
5.6
|
<0.001
|
Fibre intake g/day
|
16.0
|
15.0
|
20.6
|
0.256
|
17.2
|
15.9
|
20.3
|
0.285
|
TE = Total energy; *P- value calculated using one-way ANOVA; a Kruskal Wallis used to calculate P-values.
3.2 Micronutrient Intakes Over Time Compared with RNIs
There were significant median decreases for only the following micronutrients: vitamin B6, vitamin B1, iron, folate and magnesium. In total, 39% (56% Female) and 35% (54% females) of participants reported taking micronutrient supplements during baseline and follow-up assessments, respectively. The most common supplements reported included multivitamins, vitamin C, vitamin D, iron and calcium. A sensitivity analyses revealed that for females at baseline, there were significant differences in intakes with and without supplementation in relation to vitamin D, vitamin A, vitamin E, thiamine, riboflavin, niacin, vitamin B6, vitamin B12, folate and vitamin C intakes (Additional file 2). For males, there were significant differences in the intakes of vitamin A, vitamin D, vitamin E, vitamin C and folate between intakes with supplementation and intakes without supplementation (Additional file 2). Micronutrient intakes with and without supplementation for females differed in vitamin A, vitamin D, vitamin B1, vitamin E vitamin B6, vitamin B12, vitamin C and manganese intakes at follow-up. However, there were no differences found for males (Additional file 3).
Compared to the UK RNI, total daily intake of most nutrients were below the recommendation. Expressed as a percentage of RNI (Figure 1), daily intakes of vitamin B12, phosphorus and manganese for both sexes, and vitamin A, vitamin E, vitamin C, thiamine and niacin for females met the RNI. At follow-up, the pattern was similar for both sexes; except for phosphorus, manganese, vitamin C and vitamin B12, all other micronutrients were below the RNI.
3.3 Cross-sectional Associations
Baseline pairwise correlation found that SPPB was significantly associated with WC (r=-0.466), MNA-SF (r=0.391), BMI (r= -0.224), HGS (r =0.610), fibre (r=0.265), vitamin B6 (r= 0.303) and vitamin D (r=0.223). Nutritional status measured by MNA-SF was significantly associated with vitamin D (r=0.237) and HGS (r=0.198) (Additional file 4).
Using the results from the pairwise correlation, hierarchical multiple regression was conducted to determine the contribution of each independent variable to physical function performance and nutritional status. Table 3 shows the fully adjusted model for sociodemographic variables, nutritional status, WC, BMI and nutrient intakes as predictors of the SPPB and HGS.
Given that only fibre, vitamin D and vitamin B6 were found to be correlated with SPPB and HGS, they were the only nutrients included in the multiple regression analyses. Higher SPPB scores were associated with being younger, married, a lower WC, and higher fibre and vitamin D intakes [R2= 0.534, F (12, 87) =8.319, p<0.01]. For HGS, increasing age was significantly associated with lower HGS scores. In addition, those living in the least deprived areas (determined by the IMD scores) and reporting higher fibre intakes had significantly higher HGS scores [R2 = .0423, F= (12, 87) =5.317, p<0.01].
Table 4: Hierarchical Multiple Regression Predicting SPPB and HGS Scores (N=100)
|
SPPB
|
|
|
HGS (kg)
|
|
Predictors
|
B (95% CI)
|
SE
|
p-value
|
R2 change
|
B (95% CI)
|
SE
|
p-value
|
R2 change
|
Sociodemographic variables
|
|
|
|
|
|
|
|
|
Male vs female
|
-0.05 (-1.11, 1.01)
|
0.53
|
0.930
|
0.216
|
-3.02 (-6.56, 0.52)
|
1.78
|
0.093
|
0.342
|
Age
|
-0.09 (-0.15, -0.03)
|
0.03
|
<0.001
|
-0.45 (-0.65, -0.25)
|
0.1
|
<0.001
|
Married vs not married
|
-1.34 (-2.45, -0.24)
|
0.55
|
0.020
|
-0.53 (-1.30, 0.24)
|
0.39
|
0.177
|
IMD
|
0.14 (-0.09, 0.37)
|
0.12
|
0.240
|
-1.41 (-2.66, -0.15)
|
0.63
|
0.028
|
No education vs Education
|
-1.23 (-2.64, 0.18)
|
0.71
|
0.090
|
1.17 (-3.54, 5.88)
|
2.37
|
0.623
|
Number of diseases
|
-0.26 (-0.64, 0.11)
|
0.19
|
0.170
|
-2.87 (-6.56, 0.82)
|
1.85
|
0.125
|
Nutritional status (by MNA-SF)
|
0.39 (0.10, 0.67)
|
0.14
|
0.010
|
0.091
|
0.16 (-0.78, 1.10)
|
0.47
|
0.732
|
0.009
|
WC
|
-0.15 (-0.22, -0.09)
|
0.03
|
<0.001
|
0.152
|
-0.05 (-0.52, 0.42)
|
0.24
|
0.834
|
0.010
|
BMI
|
0.12 (-0.02, 0.26)
|
0.07
|
0.090
|
-0.06 (-0.28, 0.16)
|
0.11
|
0.579
|
Nutrient intakes
|
|
|
|
|
|
|
|
|
Fibre
|
0.08 (0.02, 0.15)
|
0.03
|
0.010
|
0.075
|
0.28 (0.07, 0.50)
|
0.11
|
0.011
|
0.061
|
Vitamin D
|
-0.04 (-0.12, 0.04)
|
0.04
|
0.340
|
-0.19 (-0.45, 0.08)
|
0.13
|
0.164
|
Vitamin B6
|
1.33 (0.35, 2.32)
|
0.49
|
0.010
|
1.33 (-1.95, 4.62)
|
1.65
|
0.421
|
Total R2
|
0.534
|
0.423
|
Note N=100; WC= Waist Circumference; IMD= Index of Multiple Deprivation; BMI= Body Mass Index; MNA-SF= Mini-Nutritional Assessment-Short Form.
A hierarchical multiple regression was conducted to test the association of nutritional (MNA-SF) and physical function while controlling for all confounders (see Table 5). This was to determine the impact of nutrient intake and physical function (SPPB and HGS) as predictors of nutritional status. SPPB was the only predictor of nutritional status, with no other variables being significantly associated with nutritional status [R2 = .0278, F=(14, 85)=2.334, p=0.009]. After controlling for all confounders, higher SPPB scores were associated with better nutritional status.
Table 5: Hierarchical Linear Regression Showing Predictors of Nutritional Status (N= 100).
Predictors
|
B (95%CI)
|
|
SE
|
P-value
|
R2 change
|
Sociodemographic variables
|
|
|
|
|
|
Male vs female
|
0.17 (-0.61, 0.96)
|
|
0.39
|
0.664
|
0.107
|
Age
|
0.02 (-0.03, 0.07)
|
|
0.02
|
0.379
|
Married vs not married
|
-0.34 (-1.16, 0.49)
|
|
0.41
|
0.420
|
IMD
|
-0.14 (-0.31, 0.03)
|
|
0.09
|
0.112
|
No education vs Education
|
0.27 (-0.78, 1.33)
|
|
0.53
|
0.607
|
Number of diseases
|
-0.24 (-0.52, 0.03)
|
|
0.14
|
0.085
|
WC
|
-0.02 (-0.04, 0.07)
|
|
0.03
|
0.535
|
0.017
|
BMI
|
0.01 (-0.11, 0.10)
|
|
0.05
|
0.953
|
Physical function
|
|
|
|
|
|
SPPB
|
0.25 (0.08, 0.42)
|
|
0.09
|
0.004
|
0.096
|
HGS
|
-0.03 (-0.08, 0.03)
|
|
0.03
|
0.253
|
Nutrient intakes
|
|
|
|
|
|
Fibre
|
0.02 (-0.03, 0.06)
|
|
0.02
|
0.530
|
0.036
|
Vitamin D
|
-0.05 (-0.11, 0.01)
|
|
0.03
|
0.092
|
Vitamin B6
|
-0.15 (-0.90, 0.59)
|
|
0.37
|
0.687
|
Total R2
|
0.256
|
WC= Waist Circumference; IMD= Index of Multiple Deprivation; BMI= Body Mass Index; HGS= Handgrip strength; SPPB= Short Performance Physical Battery
3.4 Longitudinal Associations
There were significant changes in nutritional status measured using MNA-SF, and physical function using SPPB scores over time, (Z=-2.37,p=0.018) and (Z=-4.01, p<0.001) respectively (Table 1). Per the MNA-SF cut-offs, 24% were malnourished/at-risk of malnutrition, and the remaining (76%) were normal at baseline. At follow-up, 37% were malnourished/at-risk of malnutrition, and remaining (63%) were normal. In summary, the proportion of people reporting normal status reduced, while those reporting malnourished/at-risk of malnutrition increased by 13% at follow-up. The changes in nutritional status were: 1) those who remained malnourished/at-risk of malnutrition at follow-up (12.3%); 2) those who changed to malnourished/at-risk of malnutrition at follow-up (24.7%); 3) those who changed to normal at follow-up (n=8.6%); and 4) those who remained normal at follow-up (54.3%).
Using ‘remained normal at follow-up’ as the reference category in a multinomial regression (Table 6), the results indicate no significant predictors determining the likelihood of remaining malnourished/at-risk of malnutrition as compared to the reference group. However, participants with a higher SPPB score at baseline (OR= 0.61) were less likely to change to being malnourished/at-risk of malnutrition. Conversely, participants with a higher BMI (OR=1.29) or WC (OR= 1.10) were more likely to change to being malnourished/at-risk of malnutrition compared to the reference group.
Regarding SPPB at follow-up, 35.8% remained stable, 47% had a lower score, and 17.2% improved their score. Computed into two groups, improved/stable and decline served as outcome variables in a logistic regression performed to determine the significant predictors of changes in SPPB at follow-up. The results indicate that with an increase in age (OR= 1.105, p= 0.005) or increase in the number of diseases reported (OR= 1.63, P=0.033), participants were more likely to experience a decline in physical function than to improve or remain stable. All other variables showed no significant association with the changes in physical function (See details in additional file 5).
Table 6: Multinomial regression of factors predicting nutritional status membership at follow-up (n=81)
|
Remained At Risk/Malnourished
|
Changed to At-Risk/Malnourished
|
Changed to Normal
|
|
B
|
SE
|
OR (95% CI)
|
B
|
SE
|
OR (95% CI)
|
B
|
SE
|
OR (95% CI)
|
BMI
|
0.19
|
0.2
|
1.21 (0.81-1.80)
|
0.26
|
0.12
|
1.29 (1.01-1.65)*
|
0.29
|
0.31
|
1.34 (0.72-2.48)
|
HGS
|
-0.08
|
0.1
|
0.92 (0.75-1.13)
|
-0.16
|
0.07
|
0.85 (0.74-0.98)
|
-0.16
|
0.13
|
0.85 (0.66-1.11)*
|
WC
|
-0.12
|
0.1
|
0.89 (0.73-1.08)
|
0.09
|
0.06
|
1.10 (0.97-1.24)*
|
0.39
|
0.18
|
1.48 (1.04-2.10)
|
Age
|
0.06
|
0.09
|
1.07 (0.90-1.27)
|
0.12
|
0.06
|
1.13 (1.00-1.28)
|
0.36
|
0.14
|
1.43 (1.09-1.87)
|
IMD
|
0.21
|
0.46
|
1.23 (0.50-3.05)
|
0.40
|
0.19
|
1.49 (1.03-2.16)*
|
1
|
0.38
|
2.71 (1.30-5.68)*
|
SPPB
|
-0.26
|
0.32
|
0.77 (0.41-1.45)
|
-0.49
|
0.22
|
0.61 (0.40-0.94)*
|
-0.85
|
0.45
|
0.43 (0.18-1.03)
|
Fibre
|
-0.28
|
0.15
|
0.75 (0.56-1.02)
|
-0.1
|
0.06
|
0.91 (0.81-1.02)
|
-0.07
|
0.11
|
0.93 (0.75-1.15)
|
Vitamin D
|
0.16
|
0.13
|
1.18 (0.92-1.51)
|
-0.05
|
0.1
|
0.95 (0.78-1.16)
|
0.12
|
0.18
|
1.13 (0.79-1.60)
|
Vitamin B6
|
1.17
|
1.53
|
3.23 (0.16-64.96)
|
0.05
|
0.78
|
1.05 (0.23-4.79)
|
0.11
|
1.44
|
1.12 (0.07-18.7)
|
Male
|
3.07
|
1.9
|
21.56 (0.52-892.87)
|
0.5
|
0.8
|
1.64 (0.34-7.90)
|
-1.53
|
1.86
|
0.22 (0.01-8.31)
|
Female
|
1
|
|
|
1
|
|
|
1
|
|
|
Married
|
0.2
|
1.41
|
1.22 (0.08-19.22)
|
0.93
|
0.93
|
2.54 (0.41-15.67)
|
1.76
|
2.08
|
0.25 (0.10-3.43)
|
No married
|
1
|
|
|
1
|
|
|
1
|
|
|
Educated
|
2.55
|
1.9
|
12.85 (0.31-527.7)
|
1.20
|
1.07
|
3.32 (0.41-26.83)
|
-16.41
|
0
|
0.743 (0.07-0.09)
|
No education
|
1
|
|
|
1
|
|
|
1
|
|
|
Number diseases ≤2
|
-0.54
|
1.81
|
0.59 (0.02-20.21)
|
-1.28
|
0.93
|
0.28 (0.05-1.71)
|
-2.18
|
1.9
|
0.25 (0.03-4.69)
|
Number diseases >2
|
1
|
|
|
1
|
|
|
1
|
|
|
Note Reference category: Remained Normal; OR= Odds Ratio, SE= Standard Error, 95% CI = Confidence Interval; SPPB=Short Physical Performance Battery;
IMD= Index of Multiple Deprivation; BMI= Body Mass Index; R2= 0.63 (Nagelkerke) **P value <0.01 *P value <0.05