Phenotypic and genetic subtyping of hypertension – toward personalized hypertension care

Current knowledge of phenotypic and genotypic hypertension risk factors has not been effectively translated into personalized hypertension care. The aim of this thesis was to explore hypertension subtyping by applying publicly available supervised and unsupervised subtyping algorithms to large datasets with extensive phenotyping and genotyping. This thesis included participants from two large Finnish studies: 32,442 from FINRISK and 218,792 from FinnGen. FINRISK is a cross-sectional population survey carried out every five years on risk factors for chronic, non-communicable diseases. FinnGen is a public-private partnership research project combining imputed genotype data from biobanks, patient cohorts, and prospective epidemiological surveys. Because every Finnish citizen is linked to health registers via a personal identity code, accurate follow-up is possible for all major end points, including hypertension and cardiovascular disease. In addition, we used publicly available genome-wide association data from several large-scale studies, including the UK Biobank. In FINRISK, we observed a phenotypic hypertension subgroup characterized by high blood sugar and elevated body mass index, conferring an increased risk for cardiovascular disease. In a genotyped subset of FINRISK, systolic and diastolic blood pressure polygenic risk scores improved the predictive power of an externally validated clinical hypertension risk equation. Using publicly available genetic association data, we observed four genetic hypertension components corresponding to recognizable clinical features and demonstrated their clinical relevance in FINRISK and FinnGen. In conclusion, data support the existence of a hyperglycemic hypertension subtype and robust genetic hypertension subtypes. Our findings demonstrate the current ability and future potential of genetics together with methodological development to improve personalized hypertension care.Verenpainetaudin alatyypitys fenotyypin ja genotyypin avulla Nykyistä ymmärrystä verenpainetaudin fenotyypillisistä ja geneettisistä riskitekijöistä ei ole tehokkaasti hyödynnetty verenpainetaudin yksilöllisen hoidon mahdollistamiseksi. Tämän väitöskirjatutkimuksen tavoitteena oli tutkia verenpainetaudin alatyypitystä soveltamalla julkisesti saatavilla olevia ohjatun ja ohjaamattoman oppimisen algoritmeja suuriin feno- ja genotyypitettyihin tutkimusaineistoihin. Tutkimuksessa hyödynnettiin osallistujia kahdesta suuresta suomalaisesta tutkimuksesta: 32,442 FINRISKIstä ja 218,792 FinnGenistä. FINRISKI on viiden vuoden välein toteutettava väestötutkimus kroonisten tarttumattomien tautien riski- ja suojatekijöistä. FinnGen on julkisen ja yksityisen sektorin yhteinen tutkimushanke joka yhdistää imputoitua geneettistä tietoa biopankeista, potilaskohorteista ja prospektiivisista epidemiologisista tutkimuksista. Koska jokainen Suomen kansalainen on yhdistetty terveydenhuollon rekistereihin henkilötunnuksella, pitkäaikaisseuranta on mahdollista kaikkien merkittävien päätepisteiden osalta verenpainetauti ja sydän- ja verisuonitaudit mukaan lukien. Lisäksi tutkimuksessa hyödynnettiin julkisesti saatavilla olevaa tietoa geneettisistä assosiaatioista muun muassa UK Biobank -tutkimuksesta. FINRISKIssä havaittiin fenotyypityksen perusteella verenpainetaudin alatyyppi, jonka erityispiirteitä olivat korkea verensokeri ja kohonnut painoindeksi. Alatyyppi oli yhteydessä kohonneeseen sydän- ja verisuonitautiriskiin. FINRISKIn genotyypitetyssä alaryhmässä osoitettiin, että systolisen ja diastolisen verenpaineen polygeeniset riskipisteet paransivat verenpainetaudin puhkeamista ennustavan kliinisen riskilaskurin ennustevoimaa. Hyödyntämällä julkista tietoa verenpainetaudin geneettisistä assosiaatioista havaittiin neljä verenpainetaudin geneettistä osatekijää, joiden kliininen merkitys osoitettiin FINRISKIä ja FinnGeniä apuna käyttäen. Löydökset viittaavat verenpainetaudin hyperglykeemisen fenotyypin ja usean geneettisen alatyypin olemassaoloon. Genetiikkaa ja menetelmäoppia yhdistämällä on nyt ja tulevaisuudessa mahdollista parantaa verenpainetaudin yksilöllistä hoitoa

Toward Automation of the Supine Pressor Test for Preeclampsia

Preeclampsia leads to increased risk of morbidity and mortality for both mother and fetus. Most previous studies have largely neglected mechanical compression of the left renal vein by the gravid uterus as a potential mechanism. In this study, we first used a murine model to investigate the pathophysiology of left renal vein constriction. The results indicate that prolonged renal vein stenosis after 14 days can cause renal necrosis and an increase in blood pressure (BP) of roughly 30 mmHg. The second part of this study aimed to automate a diagnostic tool, known as the supine pressor test (SPT), to enable pregnant women to assess their preeclampsia development risk. A positive SPT has been previously defined as an increase of at least 20 mmHg in diastolic BP when switching between left lateral recumbent and supine positions. The results from this study established a baseline BP increase between the two body positions in nonpregnant women and demonstrated the feasibility of an autonomous SPT in pregnant women. Our results demonstrate that there is a baseline increase in BP of roughly 10-14 mmHg and that pregnant women can autonomously perform the SPT. Overall, this work in both rodents and humans suggests that (1) stenosis of the left renal vein in mice leads to elevation in BP and acute renal failure, (2) nonpregnant women experience a baseline increase in BP when they shift from left lateral recumbent to supine position, and (3) the SPT can be automated and used autonomously

Walk This Way: Predicting Postoperative And Discharge Outcomes Among Ambulatory Surgical Patients

Within the ambulatory surgical setting, existing risk prediction models focus predominantly on postoperative factors of nausea, vomiting, and pain, but do not uniformly specify preoperative predictors of outcomes across multiple surgical specialties. Identification of preoperative markers, specifically those that are reversible, is key to improving risk stratification and designing patient-specific clinical interventions. Recent work shows that preoperative gait speed is a promising marker of postoperative morbidity and mortality within the inpatient surgical population. However, it remains to be explored whether gait speed is sensitive enough to delineate discharge and postoperative outcomes within the ambulatory surgical population. We sought to determine which specific preoperative factors independently predict discharge readiness outcomes among ambulatory surgical patients. To address this aim and following Institutional Review Board (IRB) approval, we performed a cross-sectional analysis of data from a prospective observational study of 602 ambulatory surgical patients. The primary outcomes were: 1) Time to home discharge readiness from the ambulatory post-anesthesia care unit (PACU), and 2) 24-h postoperative occurrence of nausea, vomiting and bleeding. We evaluated the occurrence of unanticipated admissions from the ambulatory PACU to ancillary care units (inpatient wards and critical care) as a post hoc secondary outcome. Preoperative measures were gait speed (6.1 m divided by the average time to walk 6.10 meters), mean arterial pressure, heart rate, demographic factors and other clinical covariates. Statistical analysis was done with SAS, version 9.2® (Cary, NC), and p\u3c0.05 was considered statistically significant. Participants were 54.3% female, the mean gait speed was 0.90 ± 0.18 m/s, and the median home discharge readiness time was 89 minutes (interquartile range 61-126). Multivariable Cox regression analyses showed that gait speed (≥1 m/s vs. \u3c 1 m/s) was an independent predictor of time to home discharge readiness after adjustment for covariates (adjusted hazard ratio = 1.25 (95% CI: 1.03-1.50), p = 0.02). For the primary outcomes, independent predictors of home discharge readiness ≤90 minutes were: preoperative heart rate, mean arterial pressure, and gait speed (adjusted odds ratio = 2.33 {95% CI: 1.52-3.54}, p\u3c0.0001), when all other covariates are held constant. Monte-Carlo Cross validation (using 2x104 iterations) showed the mean percentage of correctly classified predictions by our model was 65.6 (95% CI: 61.8-69.4). However, gait speed was not independently associated with 24-h postoperative complications, p=0.35. Predictors of unanticipated admissions included the history of cardiac surgery and prior hospitalizations, and gait speed (adjusted odds ratio = 0.54 {95% CI: 0.38-0.82}, p=0.003). We present the first cross-validated prediction model of outcomes in the ambulatory surgical setting and identify preoperative heart rate, mean arterial pressure and gait speed as three important modifiable factors, which independently associate with home discharge readiness time ≤90 minutes. Our findings underscore the importance of preoperative measures and elements of patients\u27 history for potential risk stratification and resource allocation. We conclude that a focus on reversible clinical markers may help identify those patients at risk for delayed discharge in the ambulatory surgical setting

Taking Context to Heart: Momentary Emotions, Menstrual Cycle, and Cardiac Autonomic Regulation

Background. Emotions have long been discussed in conjunction with the autonomic nervous system. Most research on heart-mood linkages does not consider sex differences or an evident underlying mechanism for sex differences- sex hormone levels. Further, most research is limited to cross-sectional and laboratory studies. The degree to which affect-autonomic associations manifest in everyday life may be different and may vary by sex and, for women, by menstrual cycle phase. This study employs ambulatory monitoring of cardiovascular measures (e.g., heart rate and heart rate variability; HRV) and concurrent emotions (e.g., sadness, stress, anxiety, anger, and happiness) in everyday life to better characterize affect-autonomic associations as a function of sex and menstrual cycle phase. Methods. Participants (N = 174) were monitored over a 5-day observation period (one 2-day and one 3-day session), using an ambulatory 24-hour electrocardiogram to monitor heart rate and ecological momentary assessment to record emotions every approximately 30 minutes. Women were monitored in both the early to mid-follicular and -luteal phases of their menstrual cycles and men in two comparably distanced sessions. Results. Multilevel models indicated that for both sexes, negative emotions and happiness were associated with elevated heart rate. Relative to men, women exhibited elevated heart rate and reduced HRV during reports of anger. For women, during the luteal phase, momentary reports of sadness, stress, and anxiety were found to predict increased heart rate and reduced HRV. Conclusion. These findings demonstrate the importance of considering sex and menstrual cycle phase in research investigating heart-mood linkages

Blood pressure, aortic stiffness, hemodynamics and cognition in twin pairs discordant for type 2 diabetes

Background: Type 2 diabetes (T2D) is associated with an increased risk of cognitive impairment and dementia with poorly understood underlying mechanisms. Objective: We examined the role of blood pressure (BP), aortic stiffness, and hemodynamics in this association. Methods: Cross-sectional sample of late middle-aged twins discordant for T2D from the Australian Twin Registry. Measurements included neuropsychological battery and brain MRI including arterial spin labelling (ASL) to measure cerebral perfusion. Mobil-o-Graph devices were used to non-invasively obtain 24-hour BP, aortic stiffness, and hemodynamic measures. Using mixed modelling, we studied associations of T2D with cognition, MRI measures, BP, aortic stiffness, and hemodynamics. Results: There were 23 twin pairs with mean age 63.7 (SD = 6.1) years. T2D (β=-0.45, p < 0.001) and age (β=-0.05, p = 0.022) were independently associated with poorer attention but not with memory or perceptual speed. T2D was associated with reduced nocturnal central systolic BP dipping (β=-3.79, p = 0.027), but not with BP, aortic stiffness, cerebral perfusion, or other hemodynamic measures. There was a statistically significant interaction between T2D and central systolic BP dipping in predicting attention scores (both p < 0.05 for the interaction term) whereby there was a positive association between BP dipping and attention scores in those with T2D, but not in those without T2D. Conclusion: We found an association between T2D and reduced nocturnal central systolic dipping, but not with any other measures of BP, stiffness or hemodynamic measures. Further study of the role of nocturnal central BP dipping in the association between T2D and cognitive impairment may help identify potential mechanisms

DYNAMIC PREDICTION OF SURVIVAL DATA USING SINGLE OR MULTIPLE LONGITUDINAL MARKERS

Recurrent events and time-to-event data occur frequently in longitudinal studies. In large clinical trials with survival endpoints, researchers collect a multitude of longitudinal markers. There is a growing need to utilize these rich longitudinal information to build prediction models and assess their prognostic performance. In this dissertation research, I propose a novel approach of integrating longitudinal markers in modeling the recurrent event or terminal event data, and conduct dynamic prediction of event risks. Under joint a model framework, I jointly model a longitudinal outcome and a recurrent event process with the two process correlated via shared latent function. The probability of having a new occurrence of recurrent event in a given time interval is predicted based on subject-specific longitudinal profile and disease history. When multivariate longitudinal outcomes are considered, traditional joint model method has limitation on specifying ap propriate longitudinal structures and computation problem occur when using Bayesian approach. To avoid these potential issues, I employ multivariate functional principal component analysis approach which is more flexible, robust and time efficient. For terminal event data, I specify a prognostic model incorporating multivariate longitudinal information, the prediction can be updated with accumulated data over time. I also propose a recurrent event model integrating multiple longitudinal markers and conduct personalized dynamic prediction of new recurrent event risk, which helps physicians to identify patients at risk and give personalized health care

Knowledge-driven feature engineering to detect multiple symptoms using ambulatory blood pressure monitoring data

Background: Hypertension is a major health concern across the globe and needs to be properly diagnosed to so it can be treated and to mitigate for this critical health condition. In this context, ambulatory blood pressure monitoring is essential to provide for a proper diagnosis of hypertension, which may not be possible otherwise due to the white coat effect or masked hypertension. In this paper, the objective is to develop a model which incorporates expert’s knowledge in the feature engineering process so as to accurately predict multiple medical conditions. As a case study, we have considered multiple symptoms related to hypertension and used an ambulatory blood pressure monitoring method to continuously acquire hypertension relevant data from a patient. The goal is to train a model with a minimum set of the most effective knowledge-driven features which are useful to detect multiple symptoms simultaneously using multi-class classification techniques. Method: Artificial intelligence-based blood pressure monitoring techniques introduce a new dimension in the diagnosis of hypertension by enabling a continuous (24hours) analysis of systolic and diastolic blood pressure levels. In this work, we present a model that entails a knowledge-driven feature engineering method and implemented an ambulatory blood pressure monitoring system to diagnose multiple cardiac parameters and associated conditions simultaneously these include morning surge, circadian rhythm, and pulse pressure. The knowledge-driven features are extracted to improve the interpretability of the classification model and machine learning techniques (Random Forest, Naive Bayes, and KNN) were applied in a multi-label classification setup using RAkEL to classify multiple conditions simultaneously. Results: The results obtained (F 1 = 0.918) show that the Random forest technique has performed well for multilabel classification using knowledge-driven features. Our technique has also reduced the complexity of the model by reducing the number of features required to train a machine learning model. Conclusion: Considering these results, we conclude that knowledge-driven feature engineering enhances the learning process by reducing the number of features given as input to the machine learning algorithm. The proposed feature engineering method considers expert’s knowledge to develop better diagnosis models which are free from misleading data-driven noisy features in some situations. It is a white-box approach in which clinicians can understand the importance of a feature while looking at its value.ue