15 social factors linked to higher chronic kidney disease risk and may help identify high-risk groups

A new large-scale prospective study using UK Biobank data shows that cumulative social disadvantage, measured by a score integrating 15 social determinants of health, is associated with a higher risk of developing chronic kidney disease (CKD) and with poor prognosis among people who already have CKD. The study, published in SCIENCE CHINA Life Sciences, also identifies plasma metabolomic and proteomic signatures that may partly explain these associations, offering clues for understanding how social factors influence kidney health.

The polysocial risk score (PsRS) was built from 15 social factors spanning economic stability, education, healthcare access, neighborhood environment, and social and community context. In this study, “social disadvantage” refers to adverse social conditions such as lower income, fewer educational opportunities, poorer access to healthcare, worse neighborhood environment, and weaker social support. These conditions may occur alone or together, and the PsRS measures their cumulative burden.

Cumulative social disadvantage and CKD risk

The researchers analyzed 279,548 participants free of CKD at baseline and 19,761 patients with established CKD in the UK Biobank. From 17 candidate social factors, they retained 15 that were associated with incident CKD and combined them into a weighted PsRS.

Over a median follow-up of about 13 years, 6,628 incident CKD cases, 3,724 CVD events and 2,486 deaths were documented. Each 1-point increase in the PsRS was associated with a 12% higher risk of incident CKD (adjusted HR 1.12; 95% CI 1.11–1.14). Among patients with CKD, each 1-point increase was associated with an 8% higher risk of CVD (adjusted HR 1.08; 95% CI 1.07–1.10) and a 12% higher risk of all-cause mortality (adjusted HR 1.12; 95% CI 1.10–1.14). Compared with participants with low social disadvantage, those with intermediate and high social disadvantage had 27% and 78% higher risks of developing CKD, respectively. Population attributable risk estimates suggested that about 24.7% of incident CKD cases, 17.0% of CVD events and 24.7% of deaths could be attributed to intermediate or high social disadvantage.

Adding the PsRS to conventional clinical prediction models modestly but significantly improved predictive performance for CKD, CVD and mortality.

Metabolomic and proteomic signatures

The study identified plasma metabolomic and proteomic features associated with the PsRS. The metabolomic signature comprised 94 metabolites, mainly involved in amino acids, fatty acids and lipoproteins. The proteomic signature comprised 428 proteins, enriched in immune and inflammatory responses, cell signaling and fibrosis-related pathways.

Exploratory mediation analyses suggested that these signatures may partly explain the associations between the PsRS and CKD incidence and poor prognosis, with the proteomic signature showing a substantially stronger mediation signal than the metabolomic signature. Unhealthy lifestyle also accounted for a smaller portion of the associations. Key mediators included omega-3 fatty acid percentage, GlycA, the degree of fatty acid unsaturation, and proteins such as IGFBP4, ACVRL1 and TNFRSF10A. The authors suggest that social disadvantage may leave a “biological imprint” through metabolic disturbances, inflammation, fibrosis and activation of signaling pathways, thereby influencing CKD development and prognosis.

Proteomic signal stands out

In multiple-mediator models that simultaneously included the metabolomic signature, proteomic signature and lifestyle score, the proteomic signature showed the most consistent mediation signal. Among participants without CKD at baseline, both the metabolomic and proteomic signatures showed independent mediation, whereas lifestyle was not significant. Among patients with CKD, only the proteomic signature retained an independent mediation effect, while the metabolomic and lifestyle scores were no longer significant. The authors note that the proteome covers broader signaling pathways and is closer to tissue-level pathological processes such as fibrosis and chronic inflammation, and may therefore better reflect the cumulative biological effects of long-term social disadvantage.

Lifestyle: modifiable but limited independent role

Unhealthy lifestyle showed some mediation in single-mediator analyses, suggesting that social disadvantage may influence behavioral risk by limiting access to healthy food, safe environments for physical activity and preventive healthcare. However, in analyses that simultaneously accounted for the metabolomic and proteomic signatures, the independent mediation effect of lifestyle was no longer significant, indicating that its risk may overlap substantially with biological pathways captured by the omics signatures. The authors emphasize that both upstream policy actions to reduce structural social inequality and individual-level lifestyle support are needed to partially offset disease risk in socially disadvantaged populations.

From risk score to precision prevention

The study suggests that the PsRS could serve as a quantifiable and scalable risk-stratification tool to help identify people at high risk of CKD and its adverse outcomes, supporting earlier monitoring and intervention. The metabolomic and proteomic features may also point to potential intervention strategies, such as omega-3 supplementation or anti-inflammatory and anti-fibrotic approaches. However, the PsRS is not a diagnostic tool and cannot replace clinical diagnosis. The authors stress that reducing CKD inequalities will require combining individual clinical management with policy-level actions, including equitable healthcare access, economic security and improved neighborhood resources.

Conclusions and outlook

Overall, the study suggests that cumulative social disadvantage is associated with CKD incidence and poor prognosis, and that metabolomic and proteomic signatures may partly explain this association, providing clues for understanding the biological pathways linking social factors to kidney health. However, the study is observational and cannot establish causality. Some social factors relied on self-report and may be subject to misclassification. The UK Biobank cohort is predominantly of European ancestry and may be affected by healthy volunteer bias, so the findings should be extrapolated with caution. Future studies should validate the predictive performance of the PsRS in multi-ethnic, community-based populations and test whether interventions targeting the identified pathways can reduce the kidney risks associated with social disadvantage through cell-based and animal models as well as randomized controlled trials.

 

Prof. Fan Fan Hou and Prof. Xianhui Qin from Nanfang Hospital, Southern Medical University, are co-corresponding authors. Dr. Yanjun Zhang from Nanfang Hospital, Southern Medical University, is the first author. This work was supported by the National Natural Science Foundation of China (82570914, 82330020 and others), the Key Technologies R&D Program of Guangdong Province (2023B1111030004) and the 111 Plan (D18005).

---

---