Abstract
AbstractBackgroundAnemia is a condition characterised by low haemoglobin concentration, which is common in elderly populations. In India, anemia is widely researched in childhood development and women of reproductive age, although studies in older populations are lacking. The Longitudinal Ageing Study India ‐ Diagnosis of Dementia (LASI‐DAD) dataset is an ideal cohort to better understand the potential impact of anaemia and changes in blood haemoglobin and cognition in an elderly dataset.Method2,758 respondents from the LASI‐DAD cohort, aged 60 or older, had venous blood samples taken for complete blood count and harmonised cognitive function tests. Haemoglobin concentration was used as the exposure in the linear regression models. Cognition data from in‐depth cognitive tests and informant interviews used the Harmonized Cognitive Assessment Protocol (HCAP). A summary value for each of the 11 tests was used as the outcome. We corrected for age and gender in the basic model with the full model also containing adjustments for rural location, smoking and BMI.ResultCases of anemia are more common in older and rural participants in the Indian population. We found a positive association between haemoglobin concentration and all the harmonised cognitive test measures except the visuospatial measures (b = 0.038 ‐ 0.072 p < 0.05). The strongest association was identified for memory‐based measures (immediate episodic, delayed episodic and recognition, b = 0.045 ‐ 0.072 p < 0.05). This was supported by an association between the general cognitive score and the other red blood count tests including the derived measure mean corpuscular haemoglobin concentration (b = 0.06 p = 0.0001) and Red Cell Distribution Width (b = ‐0.11 p < 0.0001).ConclusionWe have established for the first time in a large South Asian population that anaemia may be an important potentially modifiable risk factor for low cognitive function. As the LASI‐DAD cohort expands to include new timepoints it will be possible to run longitudinal analysis to examine the causal relationship between these measures.
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