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Background <p>There are no Indian studies estimating Cognitive Reserve (CR) across rural and urban aging populations.</p> Methods <p>We estimated CR from two ongoing aging studies in rural (CBR-SANSCOG, <i>n</i> = 4459) and urban (CBR-TLSA, <i>n</i> = 663) southern India. We used years of education (YOE), job skill level (JSL), social network diversity (SND) and multilingualism (ML) as factors and assigned weights based on their capability to predict cognitive performance (assessed using a culturally adapted cognitive test battery). We evaluated several candidate machine learning models and chose the linear regression based on its fit.</p> Findings <p>In the rural cohort, YOE, ML, and SND contributed significantly (Rural CR = 0.085×YOE + 0.184×ML + 0.030×SND), whereas YOE, ML and JSL were significant contributors for the urban cohort (Urban CR = 0.064×YOE + 0.184×ML + 0.197×JSL).</p> Interpretation <p>The contribution of CR factors differs across rural and urban Indian populations. Targeted interventions to enhance population-specific CR factors could reduce dementia risk.</p>

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Cognitive Reserve in rural and urban populations: Insights from two aging cohorts in southern India

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月1日

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  • Ramya Burra,
  • Palash K. Malo,
  • Hitesh Pradhan,
  • Raghav Prasad,
  • Pooja Rai,
  • Thomas G. Issac,
  • Jonas S. Sundarakumar,
  • Siva Athreya,
  • Rajesh Sundaresan

摘要

Background

There are no Indian studies estimating Cognitive Reserve (CR) across rural and urban aging populations.

Methods

We estimated CR from two ongoing aging studies in rural (CBR-SANSCOG, n = 4459) and urban (CBR-TLSA, n = 663) southern India. We used years of education (YOE), job skill level (JSL), social network diversity (SND) and multilingualism (ML) as factors and assigned weights based on their capability to predict cognitive performance (assessed using a culturally adapted cognitive test battery). We evaluated several candidate machine learning models and chose the linear regression based on its fit.

Findings

In the rural cohort, YOE, ML, and SND contributed significantly (Rural CR = 0.085×YOE + 0.184×ML + 0.030×SND), whereas YOE, ML and JSL were significant contributors for the urban cohort (Urban CR = 0.064×YOE + 0.184×ML + 0.197×JSL).

Interpretation

The contribution of CR factors differs across rural and urban Indian populations. Targeted interventions to enhance population-specific CR factors could reduce dementia risk.