Conceived and designed the experiments: RDR AEF UC. Performed the experiments: RDR PV SKG RJ NJ. Analyzed the data: AEF KEF. Contributed reagents/materials/analysis tools: ISY. Wrote the paper: AF RDR. Designed nutritional software for dietary data: RDR TR RJ. Provided critical revisions of important intellectual content: ISY GM MC UC TR.
The authors have declared that no competing interests exist.
Studies from the UK and North America have reported vitamin C deficiency in around 1 in 5 men and 1 in 9 women in low income groups. There are few data on vitamin C deficiency in resource poor countries.
To investigate the prevalence of vitamin C deficiency in India.
We carried out a population-based cross-sectional survey in two areas of north and south India. Randomly sampled clusters were enumerated to identify people aged 60 and over. Participants (75% response rate) were interviewed for tobacco, alcohol, cooking fuel use, 24 hour diet recall and underwent anthropometry and blood collection. Vitamin C was measured using an enzyme-based assay in plasma stabilized with metaphosphoric acid. We categorised vitamin C status as deficient (<11 µmol/L), sub-optimal (11–28 µmol/L) and adequate (>28 µmol/L). We investigated factors associated with vitamin C deficiency using multivariable Poisson regression.
The age, sex and season standardized prevalence of vitamin C deficiency was 73.9% (95% confidence Interval, CI 70.4,77.5) in 2668 people in north India and 45.7% (95% CI 42.5,48.9) in 2970 from south India. Only 10.8% in the north and 25.9% in the south met the criteria for adequate levels. Vitamin C deficiency varied by season, and was more prevalent in men, with increasing age, users of tobacco and biomass fuels, in those with anthropometric indicators of poor nutrition and with lower intakes of dietary vitamin C.
In poor communities, such as in our study, consideration needs to be given to measures to improve the consumption of vitamin C rich foods and to discourage the use of tobacco.
Vitamin C (ascorbic acid) plays a major role in human metabolism ranging from the synthesis of collagen, carnitine and norepinephrine to a large number of antioxidant activities
Participants gave full informed written consent. Illiterate subjects had the information leaflet read out to them and provided a thumb impression. The study complied with the guidelines in the Declaration of Helsinki and ethics approval was received from the Indian Council for Medical Research, Research Ethics Committees of the All India Institute of Medical Sciences, Aravind Eye Hospital, London School of Hygiene and Tropical Medicine and Queen's University Belfast.
The India age-related eye disease study (INDEYE) is a population-based study in two geographically different locations in India. The study sampling has been described in detail elsewhere
Data collection took place between September 2004 and December 2006. Enumerators collected household and individual socio-demographic and economic data. Fieldworkers interviewed participants at home with a structured questionnaire which included current and past tobacco use (smoking beedie (small hand rolled cigarettes) and/or cigarettes, chewing or inhaling), current and past alcohol use and type of cooking fuels. Diet was assessed by 24 hour recall. Within a week of the home interview participants were brought to the base hospital for the clinical examination which included anthropometry, an eye examination and blood sample collection. All examinations took place in the morning. Anthropometry included measurement of height, weight, and mid-upper arm circumference (MUAC). Participants were asked to remove heavy outer garments and take off their shoes. Standing height was measured to the nearest 0.1 cm using a portable stadiometer. Weight was measured using electronic scales and recorded to the nearest 0.1 kilogram. MUAC was measured at the mid-point between the inferior border of the acromion process (shoulder bone) and the tip of the olecranon process (elbow) to the nearest 0.1 cm on the bare left arm, using a fibre glass insertion tape. People were asked to bring medications or nutritional supplements to the hospital and details were recorded.
For each participant a sample of 15 ml blood was collected in a shaded room in two different vacutainer tubes (10 ml clotted and 5 ml EDTA unclotted). The EDTA unclotted sample was kept in the refrigerator and processed within 2 hours after collection. The EDTA samples were centrifuged at 3000 rpm at 4°C (using a cold centrifuge) for 15 minutes. After centrifugation, exactly 100 µl of plasma were transferred to each of two storage tubes using a Merck graduated pipette and exactly 900 µl of 5% metaphosphoric acid (MPA) were added to each tube and the contents were mixed by gentle inversion without shaking. The aliquoted samples were kept in the fridge until the samples of the last participant of the morning had been processed and all samples were placed in the dedicated study freezer (−70°C) for storage until shipment. Fresh MPA solution was made up every two weeks by dissolving 5 g of metaphosphoric acid crystals in 100 ml of distilled water. The solution was placed in a dark glass bottle and kept in the refrigerator at 4°C. The median time of storage of blood samples was 1.1 years. Samples were subsequently shipped by air to Queen's University Belfast in dry ice using a courier service with tracking and monitoring of sample temperature throughout the shipping process. Vitamin C was measured by automated fluorimetric assay
Assays were standardised against the US National Institute of Standards and Technology (NIST) standard reference materials. We also collected from each participants a non-fasting sample of capillary blood which was assessed for glucose (CBG) using a reagent strip test and reflectance meter.
Nutrient intakes of energy and vitamin C were calculated from the individual food items in the 24 hour recall using the Indian food composition tables
We defined MUAC values as normal (>23 in men and >22 in women) or mild malnutrition (22.1-23 in men and 20.1–22 in women) and moderate to severe malnutrition (<22 in men and <20 in women)
Statistical analysis was carried out using Stata 11 (StataCorp. 2009.
Of 7518 enumerated people aged 60 years and over, 5900 (78%) attended the hospital-based clinical examination of whom 5702 (76%) gave a blood sample. Plasma vitamin C was available in 5638 (2668 from RPC and 2970 from AEH). Those without vitamin C data (non responders to the clinical exam and those with no blood sample) were older, 69.7 years (SD = 8) compared to those with vitamin C data, 67.6 years (SD = 6), p <0.00001. There were no differences by sex, socio-economic status (or its individual components of education, caste, or landholding) or by the season of enumeration of the villages. Dietary measures of vitamin C were available in 5502 of those with plasma vitamin C. Very few people (n = 69, 1.2%) reported taking any nutritional supplements and all of these were from south India. Thirty percent of the study population (n = 1692) had plasma levels below 2 µmol/L. The majority of people with levels below 2 (n = 1184) were from north India.
The age, sex and season standardized prevalence of vitamin C deficiency was 73.9% (95% Confidence Interval, CI 70.4, 77.5) in north India and 45.7% (95% CI 42.5, 48.9) in south India (
Plasma vitamin C | Adequate | Sub-optimal | Deficient |
>28 µmol/L | 11–28 µmol/L | <11 µmol/L | |
North India | |||
N = 2668 | n = 286 | n = 403 | n = 1979 |
Prevalence | 10.8 | 15.3 | 73.9 |
95%CI | 8.0, 13.5 | 13.8, 16.8 | 70.4, 77.5 |
South India | |||
N = 2970 | n = 774 | n = 853 | n = 1343 |
Prevalence | 25.9 | 28.4 | 45.7 |
95%CI | 22.9, 28.9 | 26.3, 30.6 | 42.5, 48.9 |
North India | South India | |||
N | Prevalence | N | Prevalence | |
95% CI | 95% CI | |||
Men | 1283 | 77.7 | 1407 | 51.4 |
70.9, 84.5 | 47.4, 55.4 | |||
Women | 1385 | 70.9 | 1563 | 39.7 |
61.9, 79.8 | 33.9, 45.4 | |||
Age group | ||||
60–64 | 985 | 68.7 | 1080 | 36.6 |
60.1, 77.4 | 29.5, 43.7 | |||
65–69 | 658 | 72.2 | 864 | 39.3 |
62.9, 81.6 | 32.2, 6.3 | |||
70–74 | 552 | 80.8 | 575 | 42.1 |
73.7, 88.0 | 33.3, 50.9 | |||
75–79 | 287 | 79.0 | 275 | 44.6 |
70.8, 87.2 | 34.2, 55.0 | |||
80+ | 186 | 85.0 | 176 | 50.7 |
78.3, 91.7 | 38.2, 63.2 |
North India | South India | |||||||
Plasma vitamin C | Adequate | Sub-optimal | Deficient | p | Adequate | Sub-optimal | Deficient | p |
>28 µmol/L | 11–28 µmol/L | <11 µmol/L | >28 µmol/L | 11–28 µmol/L | <11 µmol/L | |||
N = 286 | N = 403 | N = 1979 | N = 774 | N = 853 | N = 1343 | |||
Age |
65.8 (5.9) | 66.8 (6.1) | 68.3 (6.8) | <0.0001 | 66.9 (6.2) | 67.1 (6.1) | 67.9 (6.6) | 0.002 |
Women |
182 (63.6) | 221 (54.8) | 982 (49.6) | 0.03 | 449 (58.0) | 494 (57.9) | 620 (46.2) | 0.0001 |
Lowest SES |
41 (14.3) | 77 (19.1) | 463 (23.4) | 0.02 | 139 (18.0) | 181 (21.2) | 346 (25.8) | 0.02 |
Biomass fuels |
194 (67.8) | 290 (72.0) | 1641 (82.9) | 0.001 | 349 (45.3) | 448 (53.0) | 706 (53.4) | 0.1 |
Malnutrition |
22 (7.7) | 46 (11.4) | 343 (17.3) | 0.01 | 72 (9.3) | 104 (12.2) | 254 (18.9) | <0.0001 |
Body mass Index | ||||||||
<18.5 |
50 (17.5) | 99 (24.6) | 698 (35.5) | <0.0001 | 191 (24.7) | 247 (29.1) | 476 (35.8) | 0.002 |
≥25 |
55 (19.2) | 74 (18.4) | 233 (11.8) | 0.003 | 178 (23.0) | 171 (20.2) | 206 (15.5) | 0.003 |
Diabetes |
151 (52.8) | 249 (61.8) | 1141 (57.7) | 0.1 | 447 (57.8) | 459 (53.8) | 755 (56.2) | 0.3 |
Current tobacco |
126 (44.1) | 230 (57.1) | 1284 (64.9) | <0.0001 | 248 (32.0) | 363 (42.6) | 709 (52.8) | <0.0001 |
Current alcohol |
36 (34.6) | 71 (39.0) | 447 (44.8) | 0.1 | 113 (34.8) | 122 (34.0) | 271 (37.5) | 0.6 |
Dietary vitamin C |
31.9 | 29.9 | 19.5 | <0.0001 | 35.6 | 35.0 | 33.2 | 0.02 |
18.0, 50.9 | 17.5, 44.6 | 11.8, 33.5 | 24.7, 53.7 | 25.1, 49.4 | 22.9, 48.7 |
Mean (Standard Deviation).
n with characteristic (%).
Socio-Economic status.
Moderate & severe malnutrition defined as a mid-upper arm circumference of <22 in men and <20 in women.
1283 men in north India and 1407 men in south India.
Median, (InterQuartile range) mg/day.
In Poisson regression comparing those with vitamin C deficiency with those with adequate levels, there were significant interactions with location and current tobacco use (p = 0.001) and location and season, p <0.0001) in univariable and multivariable analyses (
Univariable analysis | Multivariable analysis | |||||
PRR |
95% CI | p | PRR |
95% CI | p | |
Age Group | ||||||
60–64 | 1 | 1 | ||||
65–69 | 1.05 | 0.99, 1.10 | 1.07 | 1.02, 1.12 | ||
70–74 | 1.11 | 1.05, 1.17 | 1.08 | 1.03, 1.14 | ||
75–79 | 1.11 | 1.04, 1.19 | 1.09 | 1.02, 1.16 | ||
80+ | 1.17 | 1.09, 1.25 | 1.14 | 1.07, 1.22 | ||
P trend | <0.0001 | <0.0001 | ||||
Women | 0.91 | 0.86, 0.96 | <0.0001 | 0.93 | 0.89, 0.98 | 0.003 |
Lowest SES |
1.11 | 1.04,1 .18 | <0.0001 | 1.03 | 0.99, 1.07 | 0.2 |
Biomass fuels | 1.25 | 1.14, 1.20 | <0.0001 | 1.03 | 0.98, 1.09 | 0.02 |
Malnutrition | 1.13 | 1.07, 1.20 | <0.0001 | |||
Body mass Index | ||||||
<18.5 | 1.11 | 1.06,1 .16 | <0.0001 | 1.05 | 1.03, 1.09 | <0.001 |
≥18.5–<25 | 1 | 1 | ||||
≥25 | 0.91 | 0.84, 0.96 | 0.002 | 0.97 | 0.93, 1.02 | 0.3 |
Diabetes | 1.02 | 0.97,1.07 | 0.6 | 1.01 | 0.97, 1.05 | 0.6 |
Current tobacco |
||||||
North India | 1.09 | 1.02, 1.15 | 0.01 | 1.07 | 1.01, 1.13 | 0.02 |
South India | 1.34 | 1.19, 1.50 | <0.0001 | 1.29 | 1.18, 1.41 | <0.0001 |
Dietary vitamin C |
||||||
<18 | 1 | 1 | ||||
>18–29 | 0.88 | 0.83, 0.93 | 0.99 | 0.95, 1.03 | ||
>29–44 | 0.80 | 0.76, 0.86 | 0.95 | 0.91, 0.99 | ||
>44 | 0.73 | 0.68, 0.77 | 0.90 | 0.86, 0.94 | ||
P trend | <0.0001 | <0.0001 | ||||
Season |
||||||
North India | ||||||
December to February | 1 | 1 | ||||
March to May | 1.11 | 0.96, 1.29 | 0.2 | 1.09 | 0.95, 1.25 | 0.2 |
June to September | 1.33 | 1.21, 1.46 | <0.0001 | 1.27 | 1.16, 1.38 | <0.0001 |
October to November | 1.27 | 1.16, 1.40 | <0.0001 | 1.27 | 1.17, 1.38 | <0.0001 |
South India | ||||||
December to February | 1 | |||||
March to May | 0.86 | 0.61, 0.91 | 0.03 | 0.83 | 0.74,0.94 | 0.003 |
June to September | 0.74 | 0.61, 0.91 | 0.004 | 0.73 | 0.61,0.89 | 0.001 |
October to November | 0.87 | 0.77, 0.99 | 0.04 | 0.91 | 0.78,1.06 | 0.2 |
Prevalence rate ratios adjusted for age and sex.
Prevalence rate ratios adjusted for variables in the Table.
Socio-Economic status.
interaction for tobacco use and vitamin C deficiency by location, p = 0.001.
Quartiles of dietary vitamin C (mg/day).
interaction for season and vitamin C deficiency by location, p <0.0001.
In multivariable Poisson regression of factors associated with sub-optimal compared to adequate vitamin C status, only tobacco use and, in the north, season were associated. The PRRs were similar to those reported for vitamin C deficiency (tables available from authors on request). There was no significant interaction between location and tobacco use. The smaller number in these analyses limited the power to investigate associations and interactions.
We found a high prevalence of vitamin C deficiency in older people in India; 74% of those in the north of India and 46% in the south of India were deficient and a further 15% and 28% respectively had sub-optimal levels. In common with other studies
Use of biomass fuels was associated with vitamin C deficiency. The smoke from combustion of biomass fuels includes small respirable particles, carbon monoxide, nitrogen formaldehyde and polyaromatic hydrocarbons. Since many of the constituents of biomass fuels are also found in tobacco smoke
Seasonal differences in vitamin C deficiency varied between the north and south reflecting the different climatic and agricultural patterns across the sub-continent. In the north, the highest PRRs were observed for the main monsoon period (June to September) compared to the winter. Poor nutritional status in the monsoon months and higher dietary intakes of vegetables in the winter period have been reported from studies in the north of the sub-continent
Dietary vitamin C intakes from other studies in India
Since Vitamin C is degraded by factors such as light, temperature (above 4°C) and oxidation, considerable care is required in the collection and processing of samples
We had only a single measurement of plasma and dietary vitamin C and were unable to ascertain the effects of within person seasonal changes. Our response rates were acceptable (75%) and apart from age there was no response bias in sex or socio-economic status. Since vitamin C deficiency increased with age the prevalence of vitamin C deficiency might be underestimated.
Our population was primarily rural or from small towns, characterized by low BMI, high tobacco and biomass fuel use and low intakes of dietary vitamin C. In 15% the mid-upper arm circumference values were indicative of moderate to severe malnutrition. Our results may not apply to middle aged and younger people, city dwellers or high income groups and studies are required in these groups.
In addition to low dietary intakes of vitamin C, low plasma levels of vitamin C in India may also reflect haptoglobin (Hp) allele status (Hp1 or Hp2). The Hp2 -2 phenotype is substantially higher in India (around 70–80%) compared to populations of European ancestry (30–40%), and conversely Hp1-1 is much lower, less than 3% in India compared to around 15–20 % in Europeans
The majority of previous reports on vitamin C deficiency from population based studies have taken place in the UK or North America. In these studies the prevalence of vitamin C deficiency ranged from 26% of men and 14% of women aged 25 to 74 years in the Glasgow MONICA study
In conclusion, we found vitamin C deficiency in a substantial proportion of the older population in two settings in north and south India. Only 10% of those in the north and a quarter of those in the south met the criteria for adequate levels. Our results are relevant to current debates about the control of non-communicable diseases in India. Low fruit and vegetable intake, tobacco use and biomass fuels contribute respectively the third, fourth and fifth ranked risk factors associated with mortality and disease burden in India