Bridging the Genomic Divide: Towards Inclusive Risk Scores for Diabetic Complications in LMIC Populations, with Preliminary Metabolic Insights from a Central Asian Cohort
1
Department of Biomedical Sciences, Nazarbayev University, School of Medicine, Astana, Kazakhstan
2
Department of Medicine, Nazarbayev University, School of Medicine, Astana, Kazakhstan
3
Department of Internal Medicine, Corporate Fund “University Medical Center”, Astana, Kazakhstan
Popul. Med. 2026;8(Supplement Supplement 1):A3015
ABSTRACT
ABSTRACT:
Type 2 diabetes (T2D) complications disproportionately burden diverse low- and middle-income country (LMIC) populations [1], where multi-ethnic populations face heightened risks due to limited access to precision tools. Polygenic risk scores (PRS) for T2D and complications, predominantly derived from European-ancestry data [2], demonstrate poor transferability to diverse non-European groups, worsening health inequities and impeding precision public health. Genetic predispositions interact with modifiable factors like glycemic control and dyslipidemia to drive complications such as coronary artery disease. Poor metabolic profiles amplify atherogenic risk, yet integrated genetic-metabolic models remain underexplored for inclusive strategies. This work aims to detail a protocol for developing ethnicity-informed PRS for T2D complications across LMIC ancestries and present preliminary metabolic risk correlations from an ongoing Central Asian recruitment cohort.
METHODS:
We synthesized recent literature on PRS transferability and multi-ancestry models for T2D and complications, drawing from multi-ancestry genome-wide association studies and validation cohorts, to inform a structured scheme for an ethnically-aware PRS framework. Complementarily, we performed retrospective cross-sectional analysis of biochemical data from an underserved Central Asian T2D cohort (n=505; mean age 67.2 ± 9.2 years, females 45.1%). Spearman correlations assessed associations between HbA1c and lipid parameters. Results We outline a 3-phase protocol leveraging multi-ancestry GWAS summary statistics to develop an inclusive PRS scheme: starting with evaluation of European PRS transferability across different ancestries; development of a PRS trans-ancestry model; and validation of a hybrid PRS-metabolic predictor to advance equitable risk stratification in LMIC settings. In our cohort, poor glycemic control strongly associated with atherogenic dyslipidemia: HbA1c positively correlated with triglycerides (p = 0.001), but not with total cholesterol or LDL (p > 0.05). These findings highlight modifiable pathways where genetic risks may be amplified in diverse groups.
CONCLUSIONS:
This facilitates targeted interventions in underserved populations, advancing precision public health without borders and global equity in non-communicable disease management.
Type 2 diabetes (T2D) complications disproportionately burden diverse low- and middle-income country (LMIC) populations [1], where multi-ethnic populations face heightened risks due to limited access to precision tools. Polygenic risk scores (PRS) for T2D and complications, predominantly derived from European-ancestry data [2], demonstrate poor transferability to diverse non-European groups, worsening health inequities and impeding precision public health. Genetic predispositions interact with modifiable factors like glycemic control and dyslipidemia to drive complications such as coronary artery disease. Poor metabolic profiles amplify atherogenic risk, yet integrated genetic-metabolic models remain underexplored for inclusive strategies. This work aims to detail a protocol for developing ethnicity-informed PRS for T2D complications across LMIC ancestries and present preliminary metabolic risk correlations from an ongoing Central Asian recruitment cohort.
METHODS:
We synthesized recent literature on PRS transferability and multi-ancestry models for T2D and complications, drawing from multi-ancestry genome-wide association studies and validation cohorts, to inform a structured scheme for an ethnically-aware PRS framework. Complementarily, we performed retrospective cross-sectional analysis of biochemical data from an underserved Central Asian T2D cohort (n=505; mean age 67.2 ± 9.2 years, females 45.1%). Spearman correlations assessed associations between HbA1c and lipid parameters. Results We outline a 3-phase protocol leveraging multi-ancestry GWAS summary statistics to develop an inclusive PRS scheme: starting with evaluation of European PRS transferability across different ancestries; development of a PRS trans-ancestry model; and validation of a hybrid PRS-metabolic predictor to advance equitable risk stratification in LMIC settings. In our cohort, poor glycemic control strongly associated with atherogenic dyslipidemia: HbA1c positively correlated with triglycerides (p = 0.001), but not with total cholesterol or LDL (p > 0.05). These findings highlight modifiable pathways where genetic risks may be amplified in diverse groups.
CONCLUSIONS:
This facilitates targeted interventions in underserved populations, advancing precision public health without borders and global equity in non-communicable disease management.
| eISSN: | 2654-1459 |
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