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Prevalence and risk factors of underweight, overweight and obesity among a geriatric population living in a high-altitude region of rural Uttarakhand, India

Published online by Cambridge University Press:  15 March 2018

Aakriti Gupta
Affiliation:
Department of Human Nutrition, All India Institute of Medical Sciences, First Floor – Room No. 118, Old OT Block, New Delhi 110029, India
Umesh Kapil*
Affiliation:
Department of Human Nutrition, All India Institute of Medical Sciences, First Floor – Room No. 118, Old OT Block, New Delhi 110029, India
Ritika Khandelwal
Affiliation:
Department of Human Nutrition, All India Institute of Medical Sciences, First Floor – Room No. 118, Old OT Block, New Delhi 110029, India
Preetika Khenduja
Affiliation:
Department of Human Nutrition, All India Institute of Medical Sciences, First Floor – Room No. 118, Old OT Block, New Delhi 110029, India
Neha Sareen
Affiliation:
Department of Human Nutrition, All India Institute of Medical Sciences, First Floor – Room No. 118, Old OT Block, New Delhi 110029, India
Ravindra Mohan Pandey
Affiliation:
Department of Biostatistics, All India Institute of Medical Sciences, New Delhi, India
Ashish Datt Upadhyay
Affiliation:
Department of Biostatistics, All India Institute of Medical Sciences, New Delhi, India
*
*Corresponding author: Email [email protected]
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Abstract

Objective

To assess the prevalence and risk factors of underweight, overweight and obesity among a geriatric population living in a high-altitude region of India.

Design

Community-based cross-sectional study. Data were collected on sociodemographic profile and anthropometric parameters. Weight and height measurements were utilized for calculation of BMI. Nutrient intake data were collected using 24 h dietary recall.

Setting

High-altitude region of Nainital District, Uttarakhand State, North India.

Subjects

Community-dwelling geriatric subjects (n 981) aged 60 years or above.

Results

We found that 26·6 % of the elderly subjects were underweight (BMI<18·5 kg/m2). Overweight (BMI 25·0–29·9 kg/m2) and obesity (BMI≥30·0 kg/m2) was seen among 18·0 % and 4·6 %, respectively. After controlling for potential cofounders, risk factors such as low level of education and income, chewing problems and lower number of daily meals were found to be associated with underweight. On the other hand, risk factors for overweight/obesity were lower age, high income and unskilled work.

Conclusions

There is a need to develop and implement intervention strategies to prevent underweight, overweight and obesity among the geriatric population of India.

Type
Short Communication
Copyright
Copyright © The Authors 2018 

A high prevalence of malnutrition, as characterized by underweight (UW) and overweight (OW), has been reported among the geriatric population in India( Reference Mathew, Das and Sampath 1 Reference Saxena, Kandpal and Goel 13 ). The geriatric population has an increased risk of malnutrition due to physiological changes such as reduced metabolic rate, loss of appetite, difficulties in chewing and various co-morbidities( Reference Landi, Calvani and Tosato 14 Reference Pilgrim, Robinson and Sayer 16 ). UW among the elderly can cause impaired muscle function and immune dysfunction that increases the risk of infection and mortality( Reference Hickson 17 , Reference Soenen and Chapman 18 ). OW among the elderly, on the other hand, may contribute to the onset of chronic non-communicable diseases such as diabetes, hypertension and CHD, functional decline and disability, and increased mortality( Reference Mathew, Das and Sampath 1 , Reference Lahiri, Biswas and Santra 2 , Reference Amarya, Singh and Sabharwal 19 Reference Janssen, Heymsfield and Allison 22 ).

Earlier studies conducted in India suggest that identification of the risk factors associated with malnutrition in the geriatric population plays an important role in prevention of morbidity and mortality among them( Reference Mathew, Das and Sampath 1 Reference Saxena, Kandpal and Goel 13 ). The majority of these studies have been conducted in plains regions of the country.

Lifestyle factors such as diet and physical activity are different in plains as compared with high-altitude regions of the country. We do not have scientific evidence on the risk factors associated with UW and OW among the geriatric population living in high-altitude regions of India; hence the present study was conducted to fill the gap in the existing knowledge.

Methodology

A community-based cross-sectional study was conducted during 2015–2016 in Nainital District, Uttarakhand State, North India. A total of 981 (35·6 % males and 64·4 % females) geriatric subjects were enrolled for the study. The mean age of males was 69·5 (sd 7·4) years and of females, 67·8 (sd 7·2) years. These subjects were selected from thirty clusters (villages) identified by using population proportional to size sampling methodology, as follows: (i) all villages in Nainital District were enlisted using the information obtained from the district census office; (ii) the corresponding total number of households was obtained for each village for calculation of the cumulative population; (iii) the sampling interval was calculated using the following: sampling interval=total cumulative population/number of clusters to be studied (i.e. 30; in Nainital District, total population=955128, sampling interval=955128/30=31838); (iv) a random number of 5762 was selected between 00001 and 31838 (four digits); (v) the first village (cluster 1) in which the population was nearly equal to the random number was identified. The cluster with corresponding cumulative population of 5762 was thus selected as cluster 1; (vi) for cluster 2, 5762+sampling interval=5762+31838=37600. The cluster with corresponding cumulative population of 37600 was selected as cluster 2; (vii) for cluster 3, the cluster with corresponding cumulative population of 69 438 (37 600+31 838) was selected as cluster 3; and (viii) steps (v) and (vi) were repeated to identify the subsequent clusters.

From the selected village, one lane was selected randomly. From the selected lane, one household was selected randomly. The survey was initiated from the selected first household and contiguously covered all the required number of subjects from the cluster. If the requisite number of subjects could not be covered, then the adjacent village was covered for the remaining sample of subjects. Thirty geriatric subjects in the age group of 60 years or above were selected from each cluster by house-to-house visit. The geriatric subjects were identified with the help of village-level health and nutrition functionaries such as anganwadi workers, auxiliary nurse midwives and accredited social health activists.

The following exclusion criteria were adopted: (i) subjects who were unable to comprehend the questions objectively; and (ii) subjects who had auditory problems leading to non-response. We excluded six subjects who had difficulty in comprehension of the questions and ten subjects who had auditory problems leading to non-response. Sixteen subjects with similar sociodemographic profile, educational qualification and economic status were included in the study. An informed written consent was obtained from each subject after explanation of the objectives and data collection parameters for the study. The study was approved by the ethical committee of the All India Institute of Medical Sciences, New Delhi.

Data collection

Sociodemographic profile

A pre-tested structured questionnaire was administered to obtain identification data and sociodemographic profile such as gender, age, educational qualification, present occupation, family monthly income and financial dependency. The socio-economic status was calculated using the Kuppuswamy classification( Reference Kumar, Dudala and Rao 23 ).

Anthropometric parameters

Height and weight of the geriatric subjects were measured using standard procedures. BMI was calculated using the formula: BMI (kg/m2)=[weight (kg)]/[height (m)]2. BMI was classified as underweight (UW; <18·5 kg/m2), normal (18·5–24·9 kg/m2), overweight and pre-obese (OW; 25·0–29·9 kg/m2) and obese (OB; ≥30·0 kg/m2) as per the WHO classification( Reference Nishida 24 ).

Waist circumference (in centimetres) was assessed to identify abdominal obesity. It was measured at the smallest horizontal girth between the costal margins and the iliac crest at the end of expiration, using a SECA-212 tape, to the nearest 0·1 cm. In accordance with the WHO, waist circumference of >90 cm (35 inches) in men and >80 cm (32 inches) in women was classified as abdominal obesity( 25 ).

Dietary assessment

The dietary intake of nutrients was assessed among one-quarter of the geriatric subjects (n 248) using the one-day 24 h dietary recall method. The following steps were undertaken: (i) information regarding the meal pattern and the food items (cooked and uncooked) consumed by the subject was recorded; (ii) for each cooked food item consumed, the raw ingredients used for the preparation were recorded; (iii) equivalent quantities of raw ingredients used for preparation of each food item were weighed using a SECA kitchen scale and recorded; (iv) total volume of each cooked food item was recorded using standard cups; (v) the quantity of each food item consumed by the index subject was assessed using standard cups/spoons/chapatti models. The cups were used to aid the respondent recall the quantities consumed by the index subject; (vi) from steps (iv) and (v), the amount of raw ingredients in grams for each food item consumed by the index subject was calculated; and (vii) nutritive value of the raw foods consumed was determined using food composition tables from the Nutritive Value of Indian Foods ( 26 ). The person responsible for cooking the food was interviewed for assessing the dietary intake of the index subject. The dietary intake of macronutrients (energy, protein, fat, carbohydrate), micronutrients (Zn, Fe, Ca, Mg, K, thiamin, riboflavin, niacin, vitamin C, folic acid) and trace elements (Cu, Mn, Mo, Cr) by the geriatric subjects was compared with the RDA for Indians given by the Indian Council of Medical Research( 27 ).

Sample size

Assuming the prevalence of malnutrition to be 50 %( Reference Mathew, Das and Sampath 1 ), the desired sample size (N) was calculated with the formula:

$$N\,{\equals}\,{{z_{{{\mathop{\rm crit}\nolimits} }}^{2} \,p\left( {1\,{\minus}\,p} \right)} \over {d^{2} }},$$

where z crit is the standard normal variate corresponding to 5 % level, relative precision (p) of 5 %, design effect (d) of 2 and non-response rate of 15 %. The total sample size derived was 883 and was rounded to 900. However, we included 981 geriatric subjects in the study.

Statistical analysis

The statistical software package IBM SPSS Statistics for Windows version 20.0 was utilized for conducting the statistical analysis of the data. The χ 2 test was applied with 95 % CI to assess the association of various parameters with UW and OW among the geriatric population. To identify possible risk factors associated with UW and OW/OB, univariate linear regression analysis was performed with each factor. Multivariate logistic regression analysis was conducted on the factors that were found to be associated with UW and OW/OB to identify the independent determinants of UW and OW/OB among the geriatric population. Adjusted OR with 95 % CI were calculated. A P value less than 0·05 was considered as statistically significant.

Results

According to BMI, we found that 26·6 % of the geriatric subjects were UW. The prevalence of OW and OB was 18·0 % and 4·6 % among the geriatric subjects, respectively (Table 1). Abdominal obesity as per waist circumference was prevalent in 27·6 % of geriatric subjects.

Table 1 BMI and waist circumference, by gender, of community-dwelling geriatric subjects aged 60 years or above (n 981) living in a high-altitude region of rural Uttarakhand, India, 2015–2016

Risk factors for underweight

High prevalence of UW was found among subjects belonging to the highest age group of >80 years (37·1 %), males (34·1 %), illiterates (30·1 %), unskilled workers (32·1 %), those with lowest monthly family income (i.e. <Rs 1865; 38·3 %), those with chewing problems (39·0 %; all P<0·001) and those who consumed <2 full meals daily (29·7 %; P=0·003; Table 2).

Table 2 Sociodemographic characteristics, by nutritional status, of community-dwelling geriatric subjects aged 60 years or above (n 981) living in a high-altitude region of rural Uttarakhand, India, 2015–2016

SC/ST/OBS, Scheduled Castes/Scheduled Tribes/Other Backward Castes.

The multivariate logistic regression analysis identified male gender, level of education and income, chewing problems and lower number of daily meals as the risk factors for UW (Table 3).

Table 3 Univariate and multivariate logistic regression analysis of various risk factors of underweight and overweight/obesity v. normal weight among community-dwelling geriatric subjects aged 60 years or above (n 981) living in a high-altitude region of rural Uttarakhand, India, 2015–2016

SC/ST/OBS, Scheduled Castes/Scheduled Tribes/Other Backward Castes; Ref., reference category.

The risk for becoming UW was lower among females v. males (adjusted OR (AOR)=0·4; 95 % CI 0·3, 0·6; P≤0·001), those with high school education or above v. illiterates (AOR=0·4; 95 % CI 0·2, 0·8; P=0·009) and with increasing monthly family income v. lowest income (AOR=0·5, P=0·007 for Rs 5547–9248; AOR=0·3, P=0·007 for Rs 9249–13 873; AOR=0·5, P=0·034 for ≥Rs 13 874).

The odds of becoming UW were 2·3 (95 % CI 1·6, 3·1; P≤0·001) times higher in subjects with chewing problems (v. those without) and 1·5 (95 % CI 1·0, 2·1; P=0·028) times higher in subjects consuming <2 full meals daily (v. those consuming 3 full meals daily).

Risk factors for overweight

High prevalence of OW/OB was observed among subjects in the age group of 60–70 years (27·9 %), in the general community category (25·8 %), those with high school education or above (37·0 %), the unemployed (26·8 %) and those with monthly family income ≥Rs 13 874 (32·3 %; all P<0·001; Table 2).

After controlling for potential confounders, the risk factors associated with OW were found to be lower age, high level of education, higher income and unskilled work (Table 3). We found that the odds of becoming OW were lower with increasing age v. 60–70 years (AOR=0·4, P≤0·001 for 70–80 years; AOR=0·3, P=0·013 for >80 years), unskilled work v. unemployed (AOR=0·3; 95 % CI 0·2, 0·6, P≤0·001) and those with loss of appetite v. without appetite loss (AOR=0·6; 95 % CI 0·4, 1·0, P=0·046).

The risk for becoming OW was found to be 2·9 (95 % CI 1·6, 5·0; P≤0·001) times higher in the general community category (v. Scheduled Castes/Scheduled Tribes/Other Backward Castes), 1·9 (95 % CI 1·1, 3·5; P=0·033) times higher in subjects with high school education or above (v. illiterates) and 2·1 (95 % CI 1·2, 3·8; P=0·013) times higher in those with monthly family income of Rs 5547–9248 (v. <Rs 1865).

Dietary assessment

Table 4 presents the mean dietary intake of nutrients by geriatric subjects stratified by BMI. The mean dietary intake of all nutrients was found to be lower among UW and higher among OW/OB geriatric subjects than among those with normal BMI. We found that geriatric subjects did not meet the RDA for energy, protein, Zn, Ca (in females), Fe (in females), folic acid, niacin, riboflavin and Cu across all BMI categories.

Table 4 Mean dietary intake of nutrients, by gender and nutritional status, of community-dwelling geriatric subjects aged 60 years or above (n 248) living in a high-altitude region of rural Uttarakhand, India, 2015–2016

IQR, interquartile range.

*P value significant at 0·01, **P value significant at 0·05.

We found that male UW subjects had a significantly lower intakes of energy, Zn, Ca, P, thiamin, niacin, vitamin C, K, Cu (all P<0·05), protein, riboflavin, folic acid, Mg and Mn (all P<0·01) compared with OW/OB geriatric subjects (Table 4).

Intakes of energy, protein, carbohydrate, P, niacin, K (all P<0·05), Fe and Mn (all P<0·01) were found to be significantly higher among male OW/OB subjects than among subjects with normal BMI (Table 4).

Discussion

The present study reported a high prevalence of both UW and OW (as assessed by BMI), indicating a double burden of malnutrition, among geriatric subjects living in a high-altitude area of Uttarakhand, India. An earlier study conducted in a similar region of Uttarakhand also documented high UW and OW/OB prevalence among geriatric subjects: 35·5 and 15·9 %, respectively( Reference Saxena, Kandpal and Goel 13 ). Studies conducted in plains regions of India have similarly shown a high prevalence of UW (range of 21–55 %)( Reference Mathew, Das and Sampath 1 Reference Kalaiselvi, Arjumand and Jayalakshmy 7 ) and a higher prevalence of OW/OB (range of 26–46 %)( Reference Goswami, Nongkynrih and Kalaivani 8 Reference Ratnaprabha, Farah and Twinkle 12 ). We observed a lower prevalence of OW/OB, possibly because of higher physical activity and active lifestyle of our geriatric subjects due to the inadequate transportation facilities and difficult terrains of hilly regions of Uttarakhand. However, abdominal obesity as indicated by waist circumference was found to be high (27·6 %), suggesting increased risk of myocardial infarction, stroke, metabolic syndrome and premature death among the geriatric subjects( 25 ).

We found that the male geriatric subjects (P<0·001) had a significantly higher risk of developing UW. Earlier studies conducted in Puducherry and Haryana showed that males had 2·5 times and 0·3 times higher risk of becoming UW than females, respectively( Reference Jamir, Kalaivani and Nongkynrih 4 , Reference Ratnaprabha, Farah and Twinkle 12 ). Menopause-related weight gain, accumulation of visceral fat and adiposity in females may have accounted for lower risk of UW but higher risk of OW/OB as compared with male geriatric subjects( Reference Campos, Pedroso and Lamounier 28 ).

Studies have suggested that physiological changes related to ageing lead to decrease in both fat mass and fat-free mass after 70 years of age and decrease in height( Reference Baumgartner, Stauber and McHugh 29 , Reference Gallagher, Visser and De Meersman 30 ). This may have possibly resulted in higher prevalence of UW with increasing age (P<0·01) of the subjects.

In the present study, we found low educational status to be a significant risk factor for UW among the geriatric population (Tables 2 and 3). An earlier study conducted in Birmingham, Alabama, USA also reported lower educational status as the strongest predictor for UW( Reference Ritchie, Burgio and Locher 31 ). Conversely, a recent study conducted by Ratnaprabha et al. in Bangalore, India documented that literate subjects had 5·3 times higher risk of becoming OW/OB possibly due to better social conditions and better access to food( Reference Ratnaprabha, Farah and Twinkle 12 ). Several studies conducted in Brazil, Iran and Spain have reported increasing income as an important risk factor for OW/OB among geriatric subjects, as observed in the present study( Reference Andrade, Caldas and Kitoko 32 Reference Fisac, Lopez and Banegas 35 ), whereas geriatric subjects having less than Rs 1000/month per capita income has been associated with 2·5 times higher chances of becoming UW in an earlier study conducted by Kalaiselvi et al. in Puducherry( Reference Kalaiselvi, Arjumand and Jayalakshmy 7 ). This suggests that educational status and economic status may have an important effect on the weight status of geriatric subjects.

In the present study, other factors such as chewing difficulties (P<0·001) and lower number of daily meals (P=0·028) consumed by geriatric subjects were identified to significantly increase their chances of developing UW. A recent systematic review documented chewing problems and loss of appetite as significant risk factors for protein–energy malnutrition and UW among geriatric subjects( Reference van der Pols-Vijlbrief, Wijnhoven and Schaap 36 ). Previous studies conducted in West Bengal( Reference Lahiri, Biswas and Santra 2 ) and Tamil Nadu( Reference Vedantam, Subramanian and Rao 3 ) also reported that subjects who had decline in food intake had 2·3 and 0·4 times higher risk of developing undernutrition, respectively.

BMI has been utilized as an indicator of chronic energy deficiency and malnutrition among geriatric subjects as it reflects the percentage of body fat and fat-free mass( Reference Shetty and James 37 ). In the present study, we observed that geriatric subjects with low BMI had lower dietary intakes of nutrients than those with normal BMI. Earlier studies conducted in other countries have also reported similar associations( Reference Wahlqvist, Hsu-Hage and Kouris-Blazos 38 Reference Yim and Lee 40 ). Further weight loss due to inadequate dietary intake among these UW geriatric subjects may increase their risk of developing disability, compromised immune function, increased susceptibility to acute illnesses and reduce survival rate( Reference Defay, Delcourt and Ranvier 41 Reference Elmadfa and Meyer 43 ).

Conversely, the percentage adequacy and nutrient intakes were documented to be significantly higher among OW/OB than UW and normal-weight geriatric subjects, possibly due to overall higher quantity of food consumed by the former. The diets of the geriatric subjects were found to be high in fat density (double the RDA) and low on nutrient density. Hence, they may be at an increased risk of developing CVD and other chronic non-communicable diseases.

The strengths of the present study are as follows: (i) it is possibly the first study which has investigated the possible risk factors associated with OW/OB and UW among geriatric subjects residing in a high-altitude region of India; and (ii) it is a community-based study in which data were collected through house-to-house visit to ensure appropriate sampling. The limitation of the study is that we had to exclude subjects who were unable to comprehend the questions objectively and who had auditory problems leading to non-response.

Conclusion

In conclusion, the present study documented high prevalence of UW, OW and OB among geriatric subjects residing in a hilly region of Uttarakhand, India. Thus, there is a need to develop targeted, multifactorial interventions that aim to prevent the development of UW, as well as OW/OB, among this geriatric population. This will aid in maintenance of nutritional status among this geriatric population and delay the onset of health-related problems.

Acknowledgements

Funding support: The authors thank the Indian Council of Medical Research for providing financial support for undertaking the present study. The Indian Council of Medical Research had no role in the design, analysis or writing of this article. Conflict of interest: The authors declare no conflict of interest. Authorship: A.G. contributed to literature review, content development and manuscript development. U.K. contributed to literature review, content development, manuscript development and acts as guarantor for the paper. R.K. contributed to literature review, content development and manuscript development. P.K. contributed to literature review, content development and manuscript development. N.S. contributed to the literature review, content development and manuscript development. R.M.P. contributed to data analysis and interpretation. A.D.U. contributed to data analysis and interpretation. Ethics of human subject participation: This study was conducted according to the guidelines laid down in the Declaration of Helsinki and all procedures involving human subjects/patients were approved by the ethics committee of the All India Institute of Medical Sciences.

References

1. Mathew, AC, Das, D, Sampath, S et al. (2016) Prevalence and correlates of malnutrition among elderly in an urban area in Coimbatore. Indian J Public Health 60, 112117.Google Scholar
2. Lahiri, S, Biswas, A, Santra, S et al. (2015) Assessment of nutritional status among elderly population in a rural area of West Bengal, India. Int J Med Sci Public Health 4, 569572.Google Scholar
3. Vedantam, A, Subramanian, V, Rao, NV et al. (2010) Malnutrition in free-living elderly in rural south India: prevalence and risk factors. Public Health Nutr 13, 13281332.CrossRefGoogle ScholarPubMed
4. Jamir, L, Kalaivani, M, Nongkynrih, B et al. (2015) Anthropometric characteristics and undernutrition among older person in a rural area of Northern India. Asia Pac J Public Health 27, 22462248.Google Scholar
5. Singh, A, Sahai, D & Mathur, N (2014) A study on prevailing malnourishment among elderly population of Lucknow city. Int J Agric Food Sci Technol 5, 3540.Google Scholar
6. Virtuoso-Júnior, JS, Tribess, S, Romo-Perez, V et al. (2012) Factors associated to risk of malnutrition amongst elderly women in low-income communities. Colomb Med 43, 5462.Google Scholar
7. Kalaiselvi, S, Arjumand, Y, Jayalakshmy, R et al. (2016) Prevalence of under-nutrition associated factors and perceived nutritional status among elderly in rural area of Puducherry, South India. Arch Gerontol Geriatr 65, 156160.CrossRefGoogle ScholarPubMed
8. Goswami, AK, Nongkynrih, B, Kalaivani, M et al. (2016) Double burden of malnutrition among elderly population of Delhi. Indian J Community Health 28, 324330.Google Scholar
9. Singh, P, Kapil, U & Dey, AB (2004) Prevalence of overweight and obesity amongst elderly patients attending a geriatric clinic in a tertiary care hospital in Delhi, India. Indian J Med Sci 58, 162163.Google Scholar
10. Rajkamal, R, Singh, Z, Stalin, P et al. (2014) Prevalence and determinants of overweight and obesity among elderly population in an urban area of Puducherry. Int J Med Sci Public Health 4, 369372.Google Scholar
11. Swami, HM, Bhatia, V, Gupta, AK et al. (2005) An epidemiological study of obesity among elderly in Chandigarh. Indian J Community Med 30, 1113.Google Scholar
12. Ratnaprabha, GK, Farah, NF, Twinkle, A et al. (2015) Prevalence of obesity and its associated factors among elderly in a rural area of South Karnataka. Ann Community Health 2, 1521.Google Scholar
13. Saxena, V, Kandpal, SD, Goel, D et al. (2013) Health status of elderly a community based study. Indian J Community Health 24, 269274.Google Scholar
14. Landi, F, Calvani, R, Tosato, M et al. (2016) Anorexia of aging: risk factors, consequences, and potential treatments. Nutrients 8, 69.Google Scholar
15. Ahmed, T & Haboubi, N (2010) Assessment and management of nutrition in older people and its importance to health. Clin Interv Aging 5, 207216.Google ScholarPubMed
16. Pilgrim, AL, Robinson, SM, Sayer, AA et al. (2015) An overview of appetite decline in older people. Nurs Older People 27, 2935.CrossRefGoogle ScholarPubMed
17. Hickson, M (2006) Malnutrition and ageing. Postgrad Med J 82, 28.CrossRefGoogle ScholarPubMed
18. Soenen, S & Chapman, IM (2013) Body weight, anorexia, and undernutrition in older people. J Am Med Dir Assoc 14, 642648.Google Scholar
19. Amarya, S, Singh, K & Sabharwal, M (2014) Health consequences of obesity in the elderly. J Clin Gerontol Geriatr 5, 6367.Google Scholar
20. Zamboni, M, Mazzali, G, Zoico, E et al. (2005) Health consequences of obesity in the elderly: a review of four unresolved questions. Int J Obes (Lond) 29, 10111029.Google Scholar
21. Goswami, AK, Gupta, SK, Kalaivani, M et al. (2016) Burden of hypertension and diabetes among urban population aged ≥60 years in South Delhi: a community based study. J Clin Diagn Res 10, LC01LC05.Google Scholar
22. Janssen, I, Heymsfield, SB, Allison, DB et al. (2002) Body mass index and waist circumference independently contribute to the prediction of non-abdominal, abdominal subcutaneous, and visceral fat. Am J Clin Nutr 75, 683688.CrossRefGoogle Scholar
23. Kumar, BP, Dudala, SR & Rao, AR (2013) Kuppuswamy’s socio-economic status scale – a revision of economic parameter for 2012. Int J Res Dev Health 1, 24.Google Scholar
24. Nishida, C (2004) Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. WHO Expert Consultation. Lancet 363, 157163.Google Scholar
25. World Health Organization (2008) Waist Circumference and Waist–Hip Ratio: Report of a WHO Expert Consultation. Geneva: WHO.Google Scholar
26. Indian Council of Medical Research (2010) Nutritive Value of Indian Foods. Hyderabad: National Institute of Nutrition.Google Scholar
27. Indian Council of Medical Research (2010) Nutrient Requirements and Recommended Dietary Allowances for Indians. Hyderabad: National Institute of Nutrition.Google Scholar
28. Campos, MAG, Pedroso, ERP, Lamounier, JA et al. (2006) Nutritional status and related factors among elderly Brazilians. Rev Assoc Med Bras 52, 214221.Google Scholar
29. Baumgartner, RN, Stauber, PM, McHugh, D et al. (1995) Cross-sectional age differences in body composition in persons 60+ years of age. J Gerontol A Biol Sci Med Sci 50, 307316.CrossRefGoogle ScholarPubMed
30. Gallagher, D, Visser, M, De Meersman, RE et al. (1997) Appendicular skeletal muscle mass: effects of age, gender, and ethnicity. J Appl Physiol 83, 229239.Google Scholar
31. Ritchie, CS, Burgio, KL, Locher, JL et al. (1997) Nutritional status of urban homebound older adults. Am J Clin Nutr 66, 815818.Google Scholar
32. Andrade, FBD, Caldas, ADF, Kitoko, PM et al. (2012) Prevalence of overweight and obesity in elderly people from Vitória-ES, Brazil. Cien Saude Colet 17, 749756.Google Scholar
33. Barreto, SM, Passos, VMA & Lima-Costa, MFF (2003) Obesity and underweight among Brazilian elderly: the Bambuí Health and Aging Study. Cad Saude Publica 19, 605612.Google Scholar
34. Raeisi, A, Mehboudi, M, Darabi, H et al. (2017) Socioeconomic inequality of overweight and obesity of the elderly in Iran: Bushehr Elderly Health (BEH) Program. BMC Public Health 17, 72.Google Scholar
35. Fisac, JLG, Lopez, E, Banegas, JR et al. (2004) Prevalence of overweight and obesity in elderly people in Spain. Obes Res 12, 710715.Google Scholar
36. van der Pols-Vijlbrief, RD, Wijnhoven, HAH, Schaap, LA et al. (2014) Determinants of protein–energy malnutrition in community-dwelling older adults: a systematic review of observational studies. Ageing Res Rev 18, 112131.Google Scholar
37. Shetty, PS & James, WP (1994) Body mass index. A measure of chronic energy deficiency in adults. FAO Food Nutr Pap 56, 157.Google Scholar
38. Wahlqvist, ML, Hsu-Hage, BH-H, Kouris-Blazos, A et al. (1995) Food habits in later life – an overview of key findings. Asia Pac J Clin Nutr 4, 111.Google Scholar
39. Alam, I, Larbi, A, Pawelec, G et al. (2011) Relationship between anthropometric variables and nutrient intake in apparently healthy male elderly individuals: a study from Pakistan. Nutr J 10, 111.Google Scholar
40. Yim, KS & Lee, TY (2004) Sociodemographic factors associated with nutrients intake of elderly in Korea. Korean J Nutr 37, 210222.Google Scholar
41. Defay, R, Delcourt, C, Ranvier, M et al. (2001) Relationships between physical activity, obesity and diabetes mellitus in a French elderly population: the POLA study. Pathologies Oculaires lieés á l’Age. Int J Obes Relat Metab Disord 25, 512518.Google Scholar
42. Zohoori, N (2001) Nutrition and health functioning in the developing world. J Nutr 131, issue 9, 2429S2432S.Google Scholar
43. Elmadfa, I & Meyer, AL (2008) Body composition, changing physiological functions and nutrient requirements of the elderly. Ann Nutr Metab 52, 25.Google Scholar
Figure 0

Table 1 BMI and waist circumference, by gender, of community-dwelling geriatric subjects aged 60 years or above (n 981) living in a high-altitude region of rural Uttarakhand, India, 2015–2016

Figure 1

Table 2 Sociodemographic characteristics, by nutritional status, of community-dwelling geriatric subjects aged 60 years or above (n 981) living in a high-altitude region of rural Uttarakhand, India, 2015–2016

Figure 2

Table 3 Univariate and multivariate logistic regression analysis of various risk factors of underweight and overweight/obesity v. normal weight among community-dwelling geriatric subjects aged 60 years or above (n 981) living in a high-altitude region of rural Uttarakhand, India, 2015–2016

Figure 3

Table 4 Mean dietary intake of nutrients, by gender and nutritional status, of community-dwelling geriatric subjects aged 60 years or above (n 248) living in a high-altitude region of rural Uttarakhand, India, 2015–2016