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Latency estimation for chronic disease risk: a damped exponential weighting model.

ABSTRACT: Identifying the susceptible period when environmental factors affect disease risk is essential for understanding disease etiology. Most existing epidemiologic studies use oversimplified summaries of time-dependent exposures such as baseline or most current exposure, or the cumulative average of exposure over available follow-up periods. In this paper, we introduce a damped exponential weighting model for estimating optimal exposure weights for different time intervals. This model can accommodate flexible patterns of weights and can be fit using standard software. We applied the model to assess the latency of BMI and alcohol for post-menopausal breast cancer based on 30-year exposure history in the Nurses' Health Study. We have also performed a simulation study to assess the validity of the proposed hypothesis testing and estimation procedures in realistic conditions. We found that the type I error is close to 0.05; the bias in our parameter estimates is low and the coverage probability of interval estimates is close to 0.95. For ER+/PR+ breast cancer we found that recent BMI was a more important predictor of risk than more distant BMI; for ER-/PR- breast cancer, no latency was found and risk was characterized by cumulative high levels of BMI over a long period of time. For alcohol intake, we saw a strong positive association with cumulative intake for ER+/PR+ breast cancer; no significant association was found for cumulative intake or for any latency measure of risk for ER-/PR- breast cancer. Our results underscore the value of an easy-to-implement approach to latency analysis of exposure profiles for chronic disease.

SUBMITTER: Michels K

PROVIDER: S-EPMC7530062 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:The genetic factors affecting the natural history of nonalcoholic fatty liver disease (NAFLD), including the development of nonalcoholic steatohepatitis (NASH) and NASH-derived hepatocellular carcinoma (NASH-HCC), are still unknown. In the current study, we sought to identify genetic factors related to the development of NAFLD, NASH, and NASH-HCC, and to establish risk-estimation models for them. For these purposes, 936 histologically proven NAFLD patients were recruited, and genome-wide association (GWA) studies were conducted for 902, including 476 NASH and 58 NASH-HCC patients, against 7,672 general-population controls. Risk estimations for NAFLD and NASH were then performed using the SNPs identified as having significant associations in the GWA studies. We found that rs2896019 in PNPLA3 [p = 2.3x10-31, OR (95%CI) = 1.85 (1.67-2.05)], rs1260326 in GCKR [p = 9.6x10-10, OR (95%CI) = 1.38(1.25-1.53)], and rs4808199 in GATAD2A [p = 2.3x10-8, OR (95%CI) = 1.37 (1.23-1.53)] were significantly associated with NAFLD. Notably, the number of risk alleles in PNPLA3 and GATAD2A was much higher in Matteoni type 4 (NASH) patients than in type 1, type 2, and type 3 NAFLD patients. In addition, we newly identified rs17007417 in DYSF [p = 5.2x10-7, OR (95%CI) = 2.74 (1.84-4.06)] as a SNP associated with NASH-HCC. Rs641738 in TMC4, which showed association with NAFLD in patients of European descent, was not replicated in our study (p = 0.73), although the complicated LD pattern in the region suggests the necessity for further investigation. The genetic variants of PNPLA3, GCKR, and GATAD2A were then used to estimate the risk for NAFLD. The obtained Polygenic Risk Scores showed that the risk for NAFLD increased with the accumulation of risk alleles [AUC (95%CI) = 0.65 (0.63-0.67)].We demonstrated that NASH is genetically and clinically different from the other NAFLD subgroups. We also established risk-estimation models for NAFLD and NASH using multiple genetic markers. These models can be used to improve the accuracy of NAFLD diagnosis and to guide treatment decisions for patients.

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Latency estimation for chronic disease risk: a damped exponential weighting model.

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