An algorithm based on causal and cumulative effects of LDL and SBP to accurately calculate lifetime CV risk and benefit

Cumulative Exposure to Lipoproteins and SBP Determines Optimal Age and Intensity to Initiate Lipid and BP Lowering Therapy to Minmize Lifetime Risk of Cardiovascular Disease

Presented at the ESC congress 2020 by Brian Ference (Cambridge, UK)

Introduction and methods

Although atherosclerosis starts early in life and slowly progresses over time, current guidelines focus on identifying those with short-term risk of acute CV events. This strategy has a huge limitation for those patients who have already developed sufficient atherosclerotic burden and at high risk of developing an acute event before therapy is initiated. Development of lifetime risk equations may help to encourage younger patients to initiate prevention earlier in life. Mendelian randomization studies have demonstrated that LDL and SBP, two modifiable risk factors for CVD, have causal and cumulative effects on risk of ASCVD, that are independent and additive. Lowering of LDL and SBP earlier in life could potentially reduce the risk of lifetime risk of CVD.

The objectives of this study were to examine whether LDL and SBP have cumulative effects on risk of ASCVD and use the cumulative and causal effects of LDL and SBP on risk of ASCVD to construct a risk algorithm that can predict risk and benefit of lowering LDL and SBP, over any time horizon.

The causal effect of each mmol-year of increase in exposure to LDL and of each mmHg-year in exposure to SBP was estimated. This was done by Mendelian randomization using 60,801 CAD cases and 123.504 controls in the CARDIoGRAMplusC4D consortium. The validity was tested by assessing the effect of random allocation to higher levels of LDL and SBP during each year of life in 445,765 participant in the UK Biobank. The Mendelian randomization estimates were used to empirically develop a risk and benefit algorithm that estimates remaining life time risk for a person who survives to a given age free of CVD as a function of the average remaining life time risk among persons in the population who also survived to that age without any CVD, and a person’s cumulative exposure to LDL and SBP. Estimates of CV risk and benefits using the lifetime exposure model were compared to risk and benefit derived from existing life time risk equations, and 10-year risk equations.

Main results

• Mendelian randomization to higher LDL or SBP levels in the UK Biobank showed a stepwise increase in risk of CVD during each increasing year of exposure.
• The magnitude of the effect of each increasing mmol-year LDL and mmHg-year SBP using data from the UK Biobank were very similar to the estimates derived using summary data from the CARDIoGRAMplusC4D consortium. These findings support the cumulative exposure hypothesis on atherosclerosis.
• The algorithm using causal and cumulative estimates of LDL and SBP predicted lifetime risk of CVD at least as well as current lifetime risk and 10-year risk equations determined by Harrell’s c-index.
• The algorithm appears to provide more biological plausible individual estimates of risk and overcomes limitations of current lifetime risk and 10-year risk equations. Specifically, current life time risk equations seem to underestimate risk in young people with very high LDL-c and SBP and 10-year risk models overestimate lifetime risk in older people with very low LDL and SBP. This is similar with 10-year risk models for the prediction of 10-year risk of CVD.
• Using the algorithm leads to more accurate estimates of clinical benefit, over any time of horizon, including the legacy effect observed in long-term follow-up. In addition, the increment of increased risk with each increasing year of exposure to LDL is very similar to increment in benefit observed in each increasing year of treatment with a statin.
• Risk equations that do not include causal estimates of effect can not accurately predict benefit. Imputing results from RCTs into these equations leads to biologically implausible clinical benefit where treatment started late in life is associated with lower risk than lifelong exposure to naturally lower levels of LDL and SBP.
• Because cumulative exposure accurately predicts benefit over any time horizon, it can be used to provide an estimate when to start lowering LDL and SBP, how intensely and for how long to achieve individual target lifetime risk goal.

Conclusion

This study using data from the CARDIoGRAMplusC4D consorti and UK Biobank demonstrated that the effect of LDL and SBP on risk ASCVD increases with increased duration of exposure, thereby confirming the cumulative exposure hypothesis. An algorithm based on causal and cumulative effects of LDL and SBP was constructed to estimate lifetime risk and benefit. This algorithm overcomes the limitations of current life time risk and 10-year risk models and can estimate risk of ASCVD over any horizon and clinical benefit of lowering LDL and SBP over any time horizon.

• Our reporting is based on the information provided at the ESC congress -