The Relationship of Kidney Function, Cardiovascular Morbidity, and All-Cause Mortality: a Prospective Primary Care Cohort Study

Renal Hyperfiltration as a New Mechanism of Smoking-Related Mortality

INTRODUCTION

Renal hyperfiltration (RHF), an established risk factor for mortality, is prevalent among tobacco smokers. The aim of this study was to assess the mediating role of RHF in the association between smoking and mortality.

AIMS AND METHODS

Data of this study were retrieved from the cohort of the Kuopio Ischemic Heart Disease Risk Factor Study (KIHD), including 2064 males from Finland. Study participants were followed over a 35-year period. Using classic and counterfactual mediation analysis approaches, we estimated the mediative effect of RHF in the association between smoking and each of the following outcomes: All-cause mortality, cardiovascular disease (CVD) mortality, and non-CVD mortality.

RESULTS

The risk of all-cause mortality in smokers was twice that in nonsmokers (hazard ratio [HR], 2.06; 95% confidence interval [CI]: 1.84 to 2.31). Under the counterfactual framework the direct effect of smoking on all-cause mortality, controlled for RHF, corresponded to an HR of 2.00 (95% CI: 1.78 to 2.30). Of the effect of smoking on mortality, 5% (p-value = .016) was mediated by RHF. This finding concerned particularly non-CVD mortality.

CONCLUSIONS

RHF mediated the effect of smoking on non-CVD and all-cause mortality, but not on CVD mortality. The generalizability of our study results is however limited by its focus on a Finnish male cohort, underscoring the need for further investigation into RHF's broader implications across diverse populations.

IMPLICATIONS

This study elucidates the complex interplay between smoking, renal hyperfiltration (RHF), and mortality, offering novel insights into the mediating role of RHF. Our findings demonstrate that RHF significantly mediates the relationship between smoking and non-cardiovascular disease (non-CVD), but not CVD mortality. This distinction underscores the multifaceted role of RHF beyond its established association with cardiovascular events. By highlighting the specific pathways through which RHF mediates some of the smoking-attributed mortality, this research contributes to our understanding of the mechanisms linking smoking to mortality.

Serum and Urine Metabolites and Kidney Function

Key Points

Background

Metabolites represent a read-out of cellular processes underlying states of health and disease.

Methods

We evaluated cross-sectional and longitudinal associations between 1255 serum and 1398 urine known and unknown (denoted with “X” in name) metabolites (Metabolon HD4, 721 detected in both biofluids) and kidney function in 1612 participants of the Atherosclerosis Risk in Communities study. All analyses were adjusted for clinical and demographic covariates, including for baseline eGFR and urine albumin-creatinine ratio (UACR) in longitudinal analyses.

Results

At visit 5 of the Atherosclerosis Risk in Communities study, the mean age of participants was 76 years (SD 6); 56% were women, mean eGFR was 62 ml/min per 1.73 m2 (SD 20), and median UACR level was 13 mg/g (interquartile range, 25). In cross-sectional analysis, 675 serum and 542 urine metabolites were associated with eGFR (Bonferroni-corrected P < 4.0E-5 for serum analyses and P < 3.6E-5 for urine analyses), including 248 metabolites shared across biofluids. Fewer metabolites (75 serum and 91 urine metabolites, including seven metabolites shared across biofluids) were cross-sectionally associated with albuminuria. Guanidinosuccinate; N2,N2-dimethylguanosine; hydroxy-N6,N6,N6-trimethyllysine; X-13844; and X-25422 were significantly associated with both eGFR and albuminuria. Over a mean follow-up of 6.6 years, serum mannose (hazard ratio [HR], 2.3 [1.6–3.2], P = 2.7E-5) and urine X-12117 (HR, 1.7 [1.3–2.2], P = 1.9E-5) were risk factors of UACR doubling, whereas urine sebacate (HR, 0.86 [0.80–0.92], P = 1.9E-5) was inversely associated. Compared with clinical characteristics alone, including the top five endogenous metabolites in serum and urine associated with longitudinal outcomes improved the outcome prediction (area under the receiver operating characteristic curves for eGFR decline: clinical model=0.79, clinical+metabolites model=0.87, P = 8.1E-6; for UACR doubling: clinical model=0.66, clinical+metabolites model=0.73, P = 2.9E-5).

Conclusions

Metabolomic profiling in different biofluids provided distinct and potentially complementary insights into the biology and prognosis of kidney diseases.

Addition of bioimpedance-derived body cell mass improves performance of serum creatinine-based GFR estimation in a chronic kidney disease cohort

PURPOSE

Serum creatinine-based glomerular filtration rate (GFR) estimating equations are imprecise and systemic overestimate GFR in chronic kidney disease (CKD) populations with low muscle mass. Bioimpedance devices can measure body cell mass (BCM), a surrogate for muscle mass which has been included in a published GFR estimating equation. This BCM GFR equation is validated and compared with MDRD and CKD-EPI 2021 equations in an Indian CKD population.

METHODS

Patients with stable CKD stages 1-5 and voluntary kidney donors underwent measurement of serum creatinine, DTPA GFR and bioimpedance on the same day. BCM GFR was tested for consistency, agreement and performance with respect to DTPA GFR.

RESULTS

A total of 125 study participants were enrolled, including 106 patients with CKD (Stage 1: 8; stage 2: 32, stage 3: 42, stage 4: 20 and stage 5: 4 patients) and 19 voluntary kidney donors, with 66% males, and a mean age of 43.3 (± 16.5) years. The median bias of BCM GFR was 5.45 ml/min/1.73 m2 [95% confidence interval (CI) 4.2-8.3], absolute precision was 10.16 ml/min/1.73 m2 [95% CI 4.5-12.6], P30 was 59.1% [95% CI 50.0-67.7] and accuracy was 8.62% [95% CI 6.4-20.0]. Kappa measurement of agreement was the highest for BCM GFR-based staging (0.628 vs 0.545 for MDRD and 0.487 for CKD-EPI).

CONCLUSION

BCM-based GFR estimating equation performed better than MDRD and CKD-EPI equations in this Indian CKD population, and BCM GFR-based KDIGO staging was associated with lesser misclassification than the MDRD and CKD-EPI equations.

TRIAL REGISTRATION (PROSPECTIVE)

Clinical Trials Registry of India (CTRI/2019/11/021850).

Renal hyperfiltration revisited-Role of the individual body surface area on mortality

BACKGROUND

Higher than normal estimated glomerular filtration rate (eGFR), i.e. renal hyperfiltration (RHF), has been associated with mortality.

METHODS

A population-based screening program in Finland identified 1747 apparently healthy middle-aged cardiovascular risk subjects in 2005-2007. GFR was estimated with the creatinine-based Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation indexed for 1.73 m2 and for the actual body surface area (BSA) of the subjects. This individually corrected eGFR was calculated as eGFR (ml/min/BSA m2) = eGFR (ml/min/1.73 m2) x (BSA/1.73). BSA was calculated by the Mosteller formula. RHF was defined as eGFR of more than 1.96 SD above the mean eGFR of healthy individuals. All-cause mortality was obtained from the national registry.

RESULTS

The higher the eGFR, the greater was the discrepancy between the two GFR estimating equations. During the 14 years of follow-up, 230 subjects died. There were no differences in mortality rates between the categories of individually corrected eGFR (p = 0.86) when adjusted for age, sex, body mass index, systolic BP, total cholesterol, new diabetes, current smoking, and alcohol use. The highest eGFR category was associated with increased standardized mortality rate (SMR) when CKD-EPI formula indexed for 1.73 m2 was used, but SMR was at the population level when individually corrected eGFR was applied.

CONCLUSIONS

Higher than normal eGFR calculated by the creatinine-based CKD-EPI equation is associated with all-cause mortality when indexed to 1.73 m2, but not when indexed to actual BSA of a person. This challenges the current perception of the harmfulness of RHF in apparently healthy individuals.