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A total 2, 552 elderly participants in the CNHS during 2010-2012, aged 75 years and over, were included in the present study. General demographic and lifestyle characteristics of participants are presented in Table 1. The average age of participants was 79.42 years old. Most participants had a some type of chronic disease (74.8%) and low education levels (88.1%). The majority (62.1%) of participants had a partner and lived with their family. In this study, 1, 467 participants (57.5%) lived in urban areas and 1, 085 participants (42.5%) lived in rural villages. Participants in urban communities had significantly higher BMI than those in rural areas (P < 0.001). The proportions of participants who smoked (26.9%) and engaged in physical labor (19.2%) were higher in rural villages (P < 0.05). However, average physical activity levels and the proportion of participants with a partner (64.2%) were higher in urban communities (P < 0.05). The proportions for education and income level were significantly different between the two types of residential area (P < 0.001).
Characteristics Urban
(N = 1, 467)Rural
(N = 1, 085)Total
(N = 2, 552)P -value# Age at survey (y), χ (se) 79.28 (0.15) 79.59 (0.15) 79.42 (0.11) 0.107 BMI, χ(se) 23.63 (0.15) 22.21 (0.18) 22.95 (0.12) < 0.001 Physical activity (MET min/week), χ(se) 4, 646 (317) 3, 101 (138) 3, 905 (177) < 0.001 Physical labor, n (%) < 0.001 No 1, 412 (96.25) 895 (82.49) 2, 307 (90.40) Yes 55 (3.75) 190 (17.51) 245 (9.60) Sex, n (%) 0.924 Male 734 (50.03) 542 (49.95) 1, 276 (50.00) Female 733 (49.97) 543 (50.05) 1, 276 (50.00) Health status, n (%)† 0.291 Has a chronic disease 1, 127 (76.82) 783 (72.17) 1, 910 (74.84) No chronic disease 340 (23.18) 302 (27.83) 642 (25.16) Smoker, n (%) 0.043 No 1, 220 (83.16) 793 (73.09) 2, 013 (78.88) Yes 247 (16.84) 292 (26.91) 539 (21.12) Drinker, n (%) 0.802 No 1, 143 (77.91) 847 (78.06) 1, 990 (77.98) Yes 324 (22.09) 238 (21.94) 562 (22.02) Education level, n (%)‡ < 0.001 Low 1, 186 (80.85) 1, 061 (97.79) 2, 247 (88.05) Medium 164 (11.18) 21 (1.94) 185 (7.25) High 117 (7.98) 3 (0.28) 120 (4.70) Income level, n (%)§ < 0.001 Low 500 (34.08) 624 (57.51) 1, 124 (44.04) Medium 513 (34.97) 206 (18.99) 719 (28.17) High 348 (23.72) 222 (20.46) 570 (22.34) Unknown 106 (7.23) 33 (3.04) 139 (5.45) Marital status, n (%) 0.024 Single 525 (35.79) 443 (40.83) 968 (37.93) Has a partner 942 (64.21) 642 (59.17) 1, 584 (62.07) Note. Abbreviations: χ, mean; se, standard error; MET, metabolic equivalent. †Chronic disease indicates having at least one of the following: hypertension, dyslipidemia, or diabetes, diagnosed by measurement and biochemistry testing in this survey or by a professional doctor. ‡Education levels: low indicates illiterate, primary school or below, or junior high school; medium indicates senior high school; high indicates college or above. §Income level = annual average income: low for urban < 9, 999 RMB, rural < 5, 000 RMB; medium for urban 10, 000-19, 999 RMB, rural 5, 000-9, 999 RMB; high for urban ≥ 20, 000 RMB, rural ≥ 15, 000 RMB. #Continuous variables used t-test; categorical variables used χ2 test. Table 1. General Characteristics of Participants in Different Residential Area in this Study#
The prevalence of undernutrition was 10.5% in the elderly population aged ≥ 75 years. According to univariate analysis of the data, the positively associated lifestyle factors for underweight were living in rural areas (OR= 2.03, 95% CI: 1.43-2.90), living in the south region (OR= 1.48, 95% CI: 1.02-2.15), smoking (OR= 1.66, 95% CI: 1.19-2.32) and having a chronic disease (OR= 1.63, 95% CI: 1.19-2.22); higher income level was inversely related (OR= 0.64, 95% CI: 0.44-0.94) with undernutrition, with normal BMI as the reference group. The prevalence of underweight in rural areas (14.8%) was found to be more than twice that in urban communities (6.6%) (P < 0.001). In the north region, the prevalence of underweight was lower than that in the south region, with 7.5% and 12.0%, respectively (P < 0.05). Smoking was associated with a higher rate of underweight compared with not smoking (P= 0.003; 16.3% vs. 8.9%). A higher prevalence of underweight was also found in participants who did not have hypertension, dyslipidemia, or diabetes (P= 0.002; 16.9%) and those with low income levels (P= 0.023; 12.7%). The prevalence of undernutrition in men was 12.1% and that in women was 9.2%, and the difference was not significant. The prevalence of underweight showed no significant difference among the groups with respect to physical activity and education levels (P > 0.05). The results of the analysis are depicted in Table 2.
Characteristics No. of Participants Prevalence (%) 95%CI P-value# Lower Upper Overall 2, 552 10.5 8.6 12.4 Sex 0.089 Male 1, 276 12.1 9.7 14.5 Female 1, 276 9.2 6.9 11.5 Residential area < 0.001 Urban 1, 467 6.6 4.8 8.3 Rural 1, 085 14.8 11.7 17.9 Region 0.041 North 939 7.5 5.3 9.7 South 1, 613 12.0 9.6 14.4 Physical activity level† 0.432 Light 91 13.2 5.3 21.0 Moderate 1, 202 11.2 8.8 13.5 Heavy 1, 163 9.1 6.8 11.4 No response 96 15.7 5.9 25.5 Physical labor 0.302 No 2, 307 9.9 8.1 11.7 Yes 245 15.3 9.5 21.1 Smoker 0.003 No 2, 013 8.9 7.0 10.8 Yes 539 16.3 12.6 20.0 Drinker 0.140 No 1, 990 9.94 8.10 11.78 Yes 562 12.70 9.38 16.02 Health status 0.002 Has a chronic disease 1, 910 8.3 6.7 9.9 No chronic disease 642 16.9 12.8 21.0 Education level 0.157 Low 2, 247 10.9 9.0 12.9 Medium 185 6.1 1.4 10.7 High 120 3.3 0.0 7.6 Income level 0.023 Low 1, 124 12.7 10.2 15.2 Medium 719 8.6 5.5 11.6 High 570 7.9 5.6 10.3 Unknown 139 8.9 1.8 16.0 Marital status 0.298 Single 968 0.1 0.0 0.2 Has a partner 1, 584 6.6 5.2 8.1 Note. †Physical activity level: light means < 600 MET min/week, moderate means 600-3, 000 MET min/week, heavy means ≥ 3, 000 MET min/week. #Using SVY univariate logistic regression analysis, with normal BMI as reference. Table 2. Prevalence of Undernutrition among Elderly Chinese Adults Aged Over 75 Years with Different Lifestyle and Socioeconomic Status during 2010-2012 (weighted data)#
Figure 3 shows the result of multivariate SVY logistic regres sion analysis. The following lifestyle/socioeconomic factors had a positive association with underweight in participants aged ≥ 75 years: 1) living in rural areas (OR= 1.94, 95% CI: 1.31-2.89; P= 0.001), 2) living in the south region (OR= 1.62, 95% CI: 1.11-2.35; P= 0.012), 3) smoking (OR= 1.55, 95% CI: 1.05-2.28; P= 0.028), and 4) having no chronic diseases, i.e., hypertension, diabetes, or dyslipidemia (OR= 1.61, 95% CI: 1.15-2.21; P= 0.005).
Figure 3. Odds ratios of lifestyle and socioeconomic factors for undernutrition prevalence among elderly Chinese adults aged 75 years and over during 2010-2012 (weighted data). Models were adjusted for the variables in the figure using SVY multivariate logistic regression analysis, with normal BMI as reference.
The average energy and protein intake of this population was 1, 654 kcal/d and 50.85 g/d [0.91 g/kg BW (body weight)/d], respectively. According to Chinese Dietary Reference Intakes (DRIs), 66.1% of participants failed to meet the Estimated Energy Requirement (EER) and only 27.9% consumed sufficient protein to meet the Recommended Nutrient Intake (RNI) for protein. In this study, we found that higher rice and animal oil consumption existed in the underweight group whereas milk and vegetable oil consumption were higher in the normal-weight group (P < 0.05 or P < 0.001). There was no significant difference in energy intake, dietary protein, and fat intake between the two groups. However, a significantly higher dietary carbohydrate intake was found among participants with normal weight (P= 0.008). The percentages of energy from fat and carbohydrates were higher in the normal-weight group than those in the underweight group (P < 0.05). The percentage of energy from cereals and the proportion of fat intake from animal sources were both significantly higher in underweight participants (P = 0.004, P= 0.049). On the contrary, the proportion of fat intake from plant sources was significantly higher in individuals with normal weight (P = 0.049). The percentage of protein intake from cereals was high in the underweight group whereas the percentages of protein intake from good-quality food sources (such as legumes, animal sources, and so on) were high in the normal-weight group, even though there was no statistically significant difference between the two groups (P > 0.05). The results of this analysis are presented in Table 3.
Item Underweight Group(n= 245) Normal-weight Group (n = 1,294) Total(n =2,552) P-value χ M P25 P75 χ M P25 P75 χ M P25 P75 Food intake (g/d) Rice 194.4 147.09 56.61 300.7 138.36 100.29 40.01 200.58 133.39 93.95 38.71 193.89 <0.001 Wheat 75.4 43.58 0 104.79 96.58 66.48 16.67 145.91 100.45 72.82 21.96 148.34 <0.001 Tubers 29.45 0 0 39.65 25.25 0 0 33.33 25.56 0 0 33.33 0.609 Vegetables 224.27 200.01 115.56 300 224.84 198.93 123.33 296.66 226.19 200.01 121.74 294.44 0.575 Legumes 3.12 0 0 0 3.1 0 0 0 3.56 0 0 0 0.261 Meat 62.22 50 7.57 98 62.11 46.67 13.33 93.33 62.26 43.66 13.33 93.21 0.891 Milk 22.65 0 0 0 32.39 0 0 0 39.96 0 0 5.12 <0.001 Eggs 18.76 0 0 28 20.94 10.14 0 33.33 22.2 14.67 0 35.2 0.08 Fish 18.98 0 0 26.38 19.73 0 0 27 20.4 0 0 27.81 0.863 Vegetable oils 24.66 20.1 8.15 35 27.55 23.3 11.7 36.9 28.22 24 12.73 38.18 0.049 Animal oils 5.74 0 0 3.8 3.53 0 0 0 3.12 0 0 0 <0.001 Energy and macronutrients Energy (kcal/d) 1,716 1,634 1,280 1,980 1,646 1,542 1,217 1,967 1,654 1,556 1,234 1,971 0.05 Protein (g/d) 50.82 48.77 34.76 63.19 50.01 46.9 34.78 61.27 50.85 47.14 35.64 62.09 0.354 Protein (g/kg BW/d) 1.19 1.14 0.82 1.45 0.94 0.87 0.6 1.13 0.91 0.82 0.6 1.13 0.103 Fat(g/d) 56.55 54.03 35.21 73.17 59.72 53.53 36.02 75.8 60.16 54.31 37.17 75.92 0.352 Carbohydrate (g/d) 228.3 209.37 160.77 274.68 252.79 238.85 160.54 300.76 229.37 211.76 160.76 274.7 0.008 %E protein 12.02 11.5 9.65 13.9 12.34 11.9 9.9 14 12.43 12 10.2 14.2 0.23 %Efat 30.31 29.5 20.5 39.7 32.45 31.5 23.6 40.2 32.56 31.8 23.83 40.48 0.018 %E carbohydrate 58.15 58.60 49.15 68.3 55.75 56 47.4 64.73 55.65 55.9 47.3 64.7 0.008 Food source of energy and macronutrients %E cereals 55.25 55.5 45.05 67.6 52.02 51.85 40.1 63.63 51.45 51.45 39.5 63.3 0.004 %E legumes 1.89 0 0 2.75 2.25 0.55 0 2.8 2.2 0.5 0 2.88 0.132 %E tubers 2.05 0 0 2.60 2.03 0 0 2.7 2.1 0 0 2.70 0.796 %E animal source 13.77 11.3 5.15 21.05 15.09 13 6.08 22.43 15.22 13.1 6.2 22.3 0.156 %E oil 16.6 13.5 8.35 23.2 17.07 14.6 9.3 22.93 17.1 14.7 9.3 23.2 0.398 %E alcohol 0.53 0 0 0 0.58 0 0 0 0.5 0 0 0 0.683 %E other 9.71 6.3 4 12.55 10.49 7.25 4.18 14.23 10.89 7.7 4.3 14.6 0.137 %Protein cereals 48.08 46.7 32.4 63.35 45.78 43.4 29.4 61.23 45.01 42.6 28.83 59.7 0.101 %Protein legumes 5.69 0 0 9.1 6.61 1.8 0 9.4 6.45 1.65 0 9.5 0.166 %Protein animal 29.04 29.3 13 45.15 30.12 29 13.8 44.3 30.64 29.95 14.8 44.88 0.535 %Protein other 17.19 14.7 9.35 21.65 17.49 14.9 9.8 22.9 17.9 15.3 9.8 23.4 0.494 %Fat animal 39.7 34.7 17.15 57.6 35.64 32.4 1S.00 51.83 34.96 31.2 15.5 50.9 0.049 %Fat plant 60.3 65.3 42.2 82.8 64.36 67.6 48.18 85 65.04 68.8 49.1 84.5 0.049 Note. Abbreviations: χ, mean; M, median; P25, percentile 25; P75, percentile 75; BW, body weight. Mann-Whitney test between underweight and normal-weight groups. Table 3. Differences in Food and Dietary Macronutrient Intake between Underweight and Normal Weight Elderly Chinese Adults Aged 75 Years and Over (2010-2012)
In multivariate SVY logistic regression models, we found several factors associated with undernutrition among our elderly participants, with the normal BMI group as reference. 1) High rice (≥ 194 g/d) consumption and animal oil consumption (> 0 g/d) were positively associated with underweight (rice: OR= 2.44, 95% CI: 1.35-4.40, P= 0.023; animal fat: OR= 1.60, 95% CI: 1.18-2.17, P= 0.002). 2) A high percentage of fat intake from animal sources (≥ 50.9%) was positively associated with undernutrition, with OR= 1.56 (95% CI: 1.06-2.31). 3) High wheat consumption (≥ 149 g/d), moderate vegetable consumption (17-200 g/d), a proper proportion of energy from fat (24%-32%), and high percentage of fat intake from plant sources (≥ 84.5%) showed an inverse association with underweight (wheat: OR= 0.44, 95% CI: 0.26-0.74, P= 0.024; vegetables: OR= 0.20, 95% CI: 0.05-0.84, P= 0.027; percentage of energy from fat: OR = 0.54, 95% CI: 0.35-0.83, P= 0.005; percentage of fat from plant sources: OR= 0.67, 95% CI: 0.46-0.99, P= 0.049). All logistic regression models were adjusted for confounders such as age, residential area region, physical activity, smoking, health status, income, and energy intake. The results are presented in Table 4.
Dietary Group Q1 Q2 Q3 Q4 P-value Rice 1 1.87 (1.02-3.42) 1.42 (0.79-2.56) 2.44 (1.35-4.40) 0.023 Wheat 1 0.92 (0.57-1.50) 0.81 (0.49-1.35) 0.44 (0.26-0.74) 0.024 Tubers# 1 0.91 (0.54-1.52) 0.99 (0.66-1.49) - 0.511 Fruit# 1 0.87 (0.55-1.37) 1.04 (0.69-1.56) - 0.899 Vegetables 1 0.20 (0.05-0.84) 0.22 (0.05-1.02) 0.25 (0.05-1.14) 0.027 Legumes & 1 0.68 (0.42-1.11) - - 0.055 Meat 1 1.04 (0.66-1.63) 0.96 (0.62-1.49) 1.33 (0.83-2.12) 0.749 Milk & 1 0.71 (0.39-1.30) - - 0.266 Eggs# 1 1.12 (0.78-1.60) 1.02 (0.62-1.66) - 0.836 Fish# 1 1.11 (0.70-1.78) 1.19 (0.76-1.86) - 0.910 Vegetable oils 1 0.88 (0.56-1.36) 0.76 (0.47-1.24) 0.82 (0.50-1.34) 0.340 Animal oils & 1 1.60 (1.18-2.17) - - 0.002 Energy 1 0.92 (0.56-1.51) 1.28 (0.84-1.96) 1.03 (0.65-1.63) 0.345 Protein 1 0.79 (0.50-1.25) 1.11 (0.68-1.82) 1.39 (0.73-2.65) 0.526 Fat 1 0.81 (0.50-1.33) 1.21 (0.73-2.02) 0.91 (0.48-1.76) 0.617 %E fat 1 0.54 (0.35-0.83) 0.76 (0.50-1.14) 0.85 (0.54-1.34) 0.005 %E carbohydrates 1 0.99 (0.61-1.63) 0.74 (0.45-1.23) 1.23 (0.72-2.07) 0.665 %E protein 1 1.32 (0.91-1.92) 0.96 (0.61-1.52) 1.38 (0.82-2.27) 0.193 %E cereals 1 0.83 (0.48-1.44) 1.16 (0.67-2.01) 1.03 (0.55-1.91) 0.403 Fat from animal sources 1 1.42 (0.97-2.07) 1.14 (0.69-1.88) 1.56 (1.06-2.31) 0.041 Fat from plant sources 1 0.79 (0.50-1.25) 0.91 (0.58-1.44) 0.67 (0.46-0.99) 0.049 Note. †Models adjusted for age, residential area, region, physical activity, physical labor, smoking, health status, income, and energy intake, using SVY multivariate logistic regression analysis; reference is normal BMI. #Food intake categorical levels were none, or below or above medium intake. & Food intake categorical levels were consumption or no consumption. Table 4. Odds Ratio (95% CI) for Undernutrition Prevalence by Quartiles of Dietary Food or Macronutrient Intake among Elderly Chinese Adults Aged 75 and Over Years during 2010-2012 (weighted data)†