The relationship between presenting blood pressure in acute ischemic stroke patients and outcome is complex. Several studies have demonstrated a U-shaped curve with worse outcomes when blood pressure is high or low. The American Heart Association/American Stroke Association guidelines recommend values of blood pressure < 185/110 mmHg in patients treated with intravenous t-PA and “permissive hypertension” up to 220/120 mmHg in those not treated with intravenous t-PA. The optimal blood pressure target is less clear in patients undergoing mechanical thrombectomy. Before thrombectomy, the guidelines recommend a blood pressure < 185/110 mmHg though patients with even lower systolic blood pressures may have better outcomes. During and after thrombectomy, the guidelines recommend a blood pressure < 180/105 mmHg. However, several studies have suggested that during thrombectomy the primary goal should be to prevent significant low blood pressure . After thrombectomy, the primary goal should be to prevent high blood pressure . To make more specific recommendations, large, randomized-control studies are needed that address factors such as the baseline blood pressure, timing and degree of revascularization, status of collaterals, and estimated risk of reperfusion injury. Ischemic stroke is the fifth leading cause of death in the United States, second leading cause of death in the European Union, and leading cause of long-term disability in both . While hIn 2015, five randomized controlled trials established the safety and efficacy of endovascular mechanical thrombectomy for acute ischemic stroke from large vessel occlusion –10. MultBlood pressure is the driving force of blood flow through the tissue bed and is generated in the aorta from the contraction of the left ventricle. SBP is the maximal aortic pressure following ejection of blood from the ventricle, whereas DBP is the lowest aortic pressure just before the ventricle ejects blood again. MAP is the average pressure generated during one ventricular contraction. Using invasive intraarterial blood pressure monitoring, the MAP is calculated by integrating the area under the curve of the pressure-time waveform. With noninvasive, oscillometric blood pressure monitoring, the MAP is calculated most commonly by the equation DBP + 0.333(SBP-DBP) .lower than invasive monitoring at high blood pressures and higher than invasive monitoring at low blood pressures. In contrast, MAPs are similar when assessed by oscillometry and invasive techniques over a range of pressures [Invasive arterial blood pressure monitoring represents the gold standard as it provides continuous measurement and is preferred for critically ill patients. Noninvasive oscillometric monitoring is more common in acute ischemic stroke patients but is intermittent rather than continuous. In addition, oscillometric SBP measurements tend to be ressures .High blood pressure is common in patients with acute ischemic stroke, with over half having an SBP > 160 mmHg . Blood pSeveral studies have demonstrated a U-shaped relationship between presenting blood pressure and stroke outcome. For example, in the International Stroke Trial, both patients with SBPs < 120 mmHg and > 200 mmHg had worse outcomes. There appeared to be a “sweet spot” of optimal SBP around 150 mmHg, where the risk of death within 14 days or death or dependency at six months was the lowest. For example, for each 10 mmHg below SBP 150 mmHg, there was a 17.9% increase in early death, and for each 10 mmHg above SBP 150 mmHg, a 3.8% increase in early death .hypoperfusion with hypotension. In addition, because chronic hypertension, common among stroke patients, results in a rightward shift in the autoregulation curve, patients may be at risk for hypoperfusion even with modest drops in blood pressure. Patients may also be at risk for hyperperfusion with hypertension, which can lead to endothelial damage, cerebral edema, and intracranial hemorrhage, especially after the administration of intravenous tissue plasminogen activator (t-PA) or cerebral revascularization [Normal cerebral autoregulation occurs when small arterioles dilate and constrict depending on the cerebral perfusion pressure (CPP) . As a result, cerebral blood flow stays constant over a wide range of CPPs. When the CPP is very low, the arterioles dilate to their maximal diameter, and when very high, they constrict to their minimal diameter. During these ranges, the relationship between CPP and CBF becomes linear. Autoregulation is a complex physiological process that includes myogenic, autonomic, and metabolic mechanisms. In ischemic stroke, direct damage to the myosin within the vessel wall can impair this process . Brainstrization . Bedsiderization .non-recanalyzed patients but a linear relationship in recanalyzed patients, with higher blood pressures correlating with worse outcomes [non-recanalyzed patients, hypertension may simply reflect a persistently occluded vessel and a more severe stroke [recanalyzed patients, hypertension may lead to a worse outcome because of cerebral edema, reperfusion injury and hemorrhagic transformation.In addition to cerebral autoregulation, recanalization status also modulates the effect of blood pressure on. In a study of 674 patients treated with intravenous t-PA or endovascular intervention, Martins and colleagues showed a U-shaped relationship between blood pressures in the first 24 h and 3-month functional outcomes in outcomes  e stroke , 26. Thuinducing hypertension with volume expansion or vasopressors in normotensive patients in an attempt to augment cerebral blood flow is beneficial is uncertain. Some animal models have demonstrated reduced infarct volumes with induced hypertension [Treating frank hypotension in acute ischemic stroke patients is indicated, and guidelines recommend that correctable causes  be sought and reversed . Howeverrtension , but onlrtension , 30. Mosrtension . Theoretrtension . Also, ortension .Elevated blood pressure in the immediate post-stroke period is far more common. Before intravenous t-PA can be administered, the AHA/ASA guidelines require a blood pressure of < 185/110 mmHg and maintenance of blood pressure < 180/105 mmHg for the first 24 h following thrombolysis . WhetherFor patients not treated with intravenous t-PA, the optimal blood pressure target has yet to be well established. Multiple studies have shown that hypertension during the acute phase of stroke is associated with worse outcomes , 35, 36,Unfortunately, the results of randomized controlled trials have been mixed and have only led to confusion. For example, the Scandinavian Candesartan Acute Stroke Trial (SCAST) compared candesartan to placebo in hypertensive patients SBP > 140 mmHg) and found that active blood pressure lowering did lead to a trend towards a worse outcome . However mmHg andWhether lowering elevated blood pressure after acute stroke helps or hurts may depend on both the degree and timing of the pressure reduction. Not surprisingly, significant blood pressure reduction is more detrimental than more modest blood pressure reduction. For example, a secondary analysis of SCAST showed that a large systolic decrease of > 28 mmHg was primarily linked to the poor outcome . SimilarRegarding timing, somewhat counter-intuitively, in SCAST, the later treatment was started, the more candesartan harmed patients, whereas those treated within 6 h seemed to benefit . Also, iMechanical thrombectomy adds another variable to an already complex picture. A key factor in the effect of blood pressure on outcome in patients undergoing mechanical thrombectomy is the timing of blood pressure measurement, that is pre-, intra-, and post-procedural.All the endovascular trials included patients eligible for intravenous t-PA before the intervention. Therefore, the pre-procedural blood pressure target followed the AHA/ASA guidelines of blood pressure of < 180/105 mmHg . A post-Blood pressure management during mechanical thrombectomy is the least studied time frame. In most of the endovascular trials, intra-procedural blood pressure management protocols were absent. In the EXTEND-IA trial , the proSeveral studies have indirectly examined the role of intra-procedural blood pressure by comparing outcomes following general anesthesia vs. conscious sedation, focusing mainly on low blood pressure. Patients receiving general anesthesia had lower blood pressures and worse outcomes –50, thouThere have been three randomized trials comparing general anesthesia with conscious sedation: General or Local Anesthesia in Intra Arterial Therapy (GOLIATH) trial , Sedatiosubstantial intra-procedural blood pressure drops  were associated with worse neurologic outcomes [Several other studies confirmed that, after controlling for comorbidities and regardless of the type of anesthesia, outcomes  and incroutcomes . Contriboutcomes . In addioutcomes .It’s likely that the degree of collateralization contributes to the lower blood pressure threshold that can be tolerated. Patients with poor collaterals may be at especially high risk of hypoperfusion such that even small blood pressure drops  can increase the chances of a poor outcome . A post-While hypotension (absolute or relative) may be associated with a worse outcome, the association between intra-procedural hypertension and outcome is more complicated. In addition to MAP < 70 mmHg, analysis of the GOLIATH, SIESTA, and AnStroke trials also found that MAP > 90 mmHg for more than 45 min was also associated with worse outcomes. Similarly. John and colleagues retrospectively reviewed 147 patients who underwent endovascular intervention and found that a mean maximum intraprocedural SBP of 180.9 mmHg was an independent predictor of poor outcome . Similarleads to vessel occlusion and contributes to a poor recanalization rate. In one study, patients presenting with SBP > 185 mmHg were more likely to show persistent vessel occlusion or partial recanalization compared with those with lower blood pressures [Some have suggested that hypertension itself ressures . In a muressures . It’s beressures .The degree of collateral flow may also interact with blood pressure and affect hydromechanical forces on the clot. Specifically, in patients with poor collateral circulation, there is reduced pressure distal to the clot. Because the pressure proximal to the clot is relatively higher, clot compaction might be greater, making thrombectomy more difficult. As several studies have shown an association between higher admission blood pressure and poorer collaterals, it suggests the hypertensive response may be needed to support perfusion to the ischemic tissue even at the expense of increasing clot compaction . ConversOther studies have shown mixed results regarding whether lower or higher blood pressure during mechanical thrombectomy is better. For example, Seby and colleagues showed a lower maximum SBP (164.6 mmHg vs. 180.9 mmHg) was independently associated with a better outcome . Convershypotension appears to be linked to a worse outcome. Even small drops in blood pressure  may be associated with worse outcomes. Thus, preventing excessive drops in blood pressure during mechanical thrombectomy  should be a primary goal. Intraprocedural hypertension also seems to linked with a worse outcome. A sweet spot of SBP 140–160 mmHg may be the optimal target but prospective trials are needed to confirm this.Putting all of this together, given the pathophysiological complexity and conflicting data, it’s difficult to make sweeping generalizations. But intra-procedural Post-procedural blood pressure targets have also varied between studies. In the ESCAPE trial, “normotension” after reperfusion was targeted  and REVANumerous observational studies have shown that higher post-procedural blood pressures generally correlate with worse functional outcomes, especially in patients with poor collaterals , 71. Whesymptomatic hemorrhagic transformation was included and successful recanalization occurred in a lower percentage of patients (67% vs. 80%) [The relationship between higher post-procedure SBP and poor outcomes is mainly because of greater reperfusion injury, however, high post-procedural blood pressure has been inconsistently associated with hemorrhagic transformation. This is likely because of differences in inclusion criteria (anterior circulation only or both anterior and posterior circulation), recanalization status, and definition of hemorrhagic transformation. For example, Mistry and colleagues, in a study of anterior circulation vessel occlusions, showed that maximum SBP within the first 24 h after thrombectomy independently correlated with a worse outcome at three months and hemorrhagic transformation (symptomatic and asymptomatic) at 48 h. Supporting the theory that hemorrhage is tied to reperfusion injury, hemorrhages occurred at lower mean SBPs in patients who had successful recanalization . A post-vs. 80%) . Similarvs. 80%) . In the vs. 80%) .A recent meta-analysis of 25 studies and 6,474 patients by Malhotra and colleagues examining pre-, intra-, and post-procedural blood pressure and outcome concluded broadly that, before thrombectomy, higher mean SBP levels were associated with a lower odds of 3-month independence and increased mortality. During thrombectomy, higher maximum SBP levels were associated with lower odds of 3-month independence. There was no association between low SBP and outcome although only three trials , 50, 63 Finally, in a sub study of the BEST trial, higher BPV in the first 24 h after thrombectomy was associated with a worse 90-day outcome, especially for systolic BPV and in patients with successful recanalization. Interestingly, in patients treated with intravenous antihypertensive medications, while the mean blood pressure dropped, the BPV increased . FurtherDespite the fundamental relationship between blood pressure and cerebral blood flow, the optimal management of blood pressure in acute ischemic stroke is not straight forward. For patients receiving intravenous thrombolysis, the AHA/ASA guidelines recommend a blood pressure < 185/100 mmHg in order to decrease the risk of hemorrhagic transformation. For those not receiving thrombolysis, the guidelines recommend “permissive hypertension” up to 220/120 mmHg though studies of blood pressure lowering have yielded mixed results. This is likely because of heterogenous patient populations and various other confounding factors including, baseline blood pressure, timing of measurement and intervention, cerebral autoregulation and recanalization status. For ischemic stroke patients undergoing mechanical thrombectomy, the optimal blood pressure target which both maintains cerebral perfusion of ischemic tissue while avoids reperfusion injury and hemorrhagic transformation remains unclear. Some general recommendations are as follows:Before mechanical thrombectomy, for patients having received intravenous t-PA, a blood pressure target of < 185 mmHg is recommended, as per AHA/ASA guidelines. Patients with even lower blood pressures (e.g. < 160 mmHg) may have better outcomes .low blood pressure . The upper range target is not clear but patients with very high SBP appear to have a worse outcome and so targeting a SBP in the 140–160 mmHg range may be optimal especially in the setting of good collaterals.During mechanical thrombectomy, the primary goal should be to prevent significant high blood pressure  as there is convincing data that higher SBPs lead to a worse functional outcomes and higher risk of hemorrhagic transformation. This is particularly true following successful vessel recanalization. In these patients, lowering the post-procedure blood pressure more aggressively  may be beneficial.Following mechanical thrombectomy, the primary goal should be to prevent Ultimately, hemodynamic goals in patients with ischemic stroke undergoing mechanical thrombectomy should be individualized based on the baseline blood pressure, timing and degree of revascularization, status of collaterals, and estimated risk of reperfusion injury. In addition, measurement of the cerebral autoregulation status may also be helpful in targeting an optimal blood pressure. Blood pressure variability is an emerging risk factor poor outcome after ischemic stroke and following mechanical thrombectomy. In the future, large, high-quality randomized-control studies are needed that carefully address these all of these factors.


Aims: Implementation of guideline-recommended pharmacological treatment in heart failure (HF) patients remains challenging. In 2021, the European Heart Failure Association (HFA) published a consensus document in which patient profiles were created based on readily available patient characteristics and suggested that treatment adjusted to patient profile may result in better individualized treatment and improved guideline adherence. This study aimed to assess the distribution of these patient profiles and their treatment in a large real-world chronic HF cohort.Methods and results: The HFA combined categories of heart rate, blood pressure, presence of atrial fibrillation, chronic kidney disease, and hyperkalemia into eleven phenotypic patient profiles. A total of 4,455 patients with chronic HF and a left ventricular ejection fraction ≤40% with complete information on all characteristics were distributed over these profiles. In total, 1,640 patients (36.8%) could be classified into one of the HFA profiles. Three of these each comprised >5% of the population and consisted of patients with a heart rate >60 beats per minute with normal blood pressure (>90/60 mmHg) and no hyperkalemia.Conclusion: Nearly forty percent of a real-world chronic HF population could be distributed over the eleven patient profiles as suggested by the HFA. Phenotype-specific treatment recommendations are clinically relevant and important to further improve guideline implementation. Heart failure (HF) is often characterized as a global pandemic, with recent reports estimating the number of HF patients at 64.3 million. . HF is aThis study aimed to assess the distribution and treatment characteristics of the patient profiles as previously proposed by the HFA in a large real-world chronic HF cohort.Data from the CHECK-HF registry were used to identify the patient profiles. The design and methods of the registry have been published previously. . In shorFor this analysis, patients with a left ventricular ejection fraction (LVEF) ≤40% were selected, leaving a total of 6,256 patients. Next, only patients with available information on heart rate, systolic and diastolic blood pressure, serum potassium, estimated glomerular filtration rate and atrial fibrillation were analyzed and distributed over the HFA profiles .In order to create the profiles, the HFA combined heart rate , blood pressure , atrial fibrillation , chronic kidney disease (CKD) and/or hyperkalemia  which would add up to a total of 36 potential profiles when different categories of these parameters are combined. However, the HFA only based seven profiles on these categories, and added HR>60, potassium >5.5, estimated glomerular filtration rate (eGFR) >30 mL/min/1.73m2 or eGFR 30–60 mL/min/1.73m2 as new categories for their remaining four profiles, making a total of eleven patient profiles . This im2 and/or a serum potassium >5.0, while no CKD/HK was defined as an eGFR ≥60 and a serum potassium ≤5.0. Atrial fibrillation comprised all forms of AF .The BP category <90/60 mmHg (hypotension) was defined as an SBP <90 mmHg and/or a DBP <60 mmHg while SBP had to be <140 mmHg. Hypertension was defined as an SBP ≥140 mmHg and/or DBP ≥90 mmHg. The BP category >90/60 mmHg (normotension) was defined as an SBP ≥90 mmHg but <140 mmHg and a DBP ≥60 mmHg but <90 mmHg. For heart rate, HR <60, HR 60–70, and HR >70 beats per minute were used as categories. CKD/HK was defined as an eGFR <60 30–60 mL/min/1.73 m2.A total of 4,455 patients with an LVEF ≤40% were analyzed. The distribution of the patient characteristics is shown in The eleven profiles postulated by the HFA comprised a total of 1,640 patients . Of these profiles, three seemed to be most relevant with a combined prevalence of 24.8%, all with a heart rate >60 beats per minute (bpm), BP >90/60 mmHg and no hyperkalemia. Detailed information about the prevalence of each profile is provided in The remaining 2,814 patients  could not be classified in the HFA profiles and were fitted into one of the 29 additional profiles. The prevalence of the additional profiles also varied strongly, but the largest four profiles together comprised nearly a third of the total population, and mostly consisted of patients with a BP >90/60 and CKD. Thirteen out of the 29 additional profiles had a prevalence lower than one percent of the population. Information about the additional profiles is presented in Detailed information on medical therapy, including prescription rates and prescribed doses, for each HFA profile is shown in According to the latest HF guidelines, clinicians have four cornerstone drugs with a Class 1 recommendation for the treatment of patients with HFrEF. . The genWhile the increasing body of evidence suggests initiating all four drug classes as rapidly as possible, there are several possible strategies to achieve this. Beldhuis et al. suggest starting the drug classes in the order of SGLT2i, MRA, ARNI, and then betablocker as fast as possible. . Packer 1. Patients with low blood pressure and high heart rate: This hemodynamic phenotype includes HFA profile 6 . The HFA recommends modification of GDMT in case of symptomatic hypotension, which is in line with the practical guidance of the latest guidelines. For this profile, the HFA recommends that, despite low systolic blood pressure, betablockers should be up-titrated to the target dose or maximum tolerated dose. Furthermore, they state that MRAs have a minimal effect on blood pressure, and rarely need to be discontinued. Our results show that there is still much room for implementation and up-titration of betablockers and, mainly, of MRAs (prescribed in 54.1% of the patients of whom only 20% received the guideline-recommended dose), especially considering the fact that the patients in this profile had a normal kidney function.2. Patients with low blood pressure and low heart rate: This phenotype corresponds with HFA profile 1 , and according to the recommendations, withdrawal of MRAs is not necessary, while reduction of betablocker dose may be necessary in case of a heart rate <50 bpm or symptomatic bradycardia. Our analysis showed that 83.3% of the patients in this profile were treated with a betablocker, but only 57% received an MRA which indicates possibilities for further treatment optimization.3. Patients with normal blood pressure and low heart rate: This phenotype refers to HFA profile 2, which had a prevalence of 3.8% in our study. The HFA recommends betablockers and/or ivabradine to be down-titrated in case of symptomatic bradycardia or heart rate <50 bpm. Eighty-two percent of the patients in this profile were treated with a betablocker, of whom 13.9% received the guideline-recommended dose. As blood pressure and kidney function were normal in this profile, there is the possibility of up-titration of ACEi/ARB and MRA.4. Patients with normal blood pressure and high heart rate: This phenotype corresponds with HFA profile 7, which was one of the most relevant profiles with a prevalence of nearly 11%. The HFA recommends treatment with target doses of betablockers, and up-titration of ACEi/ARB or ARNI to target dose as well. Our results show that 79.4% of the patients in this profile were treated with a betablocker, of whom only 19.7% received the guideline-recommended dose. Considering the high heart rate (>70 bpm) and absence of atrial fibrillation, it is striking that such a small proportion of patients were treated with guideline-recommended betablocker therapy. Furthermore, whilst 87.2% were treated with ACEi/ARB, only 39.9% received the target dose, which indicates room for further improvement, especially in light of absence of chronic kidney disease.5. Patients with atrial fibrillation and normal blood pressure: This phenotype is captured in HFA profile eight and was quite common with a prevalence of 5.8%. Atrial fibrillation is frequently seen in patients with chronic HF, which was also shown in our cohort with a prevalence of 24% . The HFA6. Patients with atrial fibrillation and low blood pressure: This phenotype was described as HFA profile 4, which was very uncommon in our population . The average heart rate was 66 bpm, whereas a heart rate >70 bpm should be aimed for according to the recommendations. The HFA recommends betablockers to be reduced or discontinued if necessary. Our results indicated that 75% used a betablocker of whom 11.1% at the full guideline-recommended dose, and with an average heart rate of 66 bpm, it appears that down-titration of betablockers in this profile could be beneficial. The expected increase in blood pressure could enable initiation and up-titration of ACEi/ARB as only 66.7% received these, of whom 62.5% at the recommended dose. Strikingly, 75% used an MRA, but no patient received the recommended dose.7. Patients with chronic kidney disease (CKD): As shown in our cohort, CKD is frequently encountered in daily clinical practice, and it may inhibit GDMT implementation. HFA profiles 9 and 10 consisted of patients with CKD (including patients with an eGFR<30 and eGFR 30–60), and together accounted for 9.8% of our cohort. The HFA stated that all foundational HF drugs can be given down to an eGFR of 30. However, our analysis showed that in HFA profile 10 (eGFR 30–60), only 81.1% used a betablocker, 63% an MRA and 85.9% an ACEi/ARB, and mainly at lower than recommended doses. Therefore, there appears to be ample room for drug optimization in this particular profile. Interestingly, diuretics were often prescribed in these profiles .8. Pre-discharge patient: Unfortunately, the current registry only included patients in outpatient setting, so information for this subgroup was unavailable.9. Patients with hypertension despite guideline-directed medical therapy: As shown in As discussed above, the HFA provided treatment recommendations for several phenotypes that may encompass multiple patient profiles. In this study, we showed that the HFA profiles comprise nearly forty percent of the population. The added value of patient profiling is to provide more specific recommendations on top of general guideline recommendations for patients that may require a more personalized approach. We have also identified additional profiles that together accounted for 63% of our HF population. In their position paper, the HFA clearly stated that patients may not always be fitted into one particular profile based upon these parameters, and that certain profiles may need to be combined and compared for personalized advice. . ConsideIn short, our analysis showed that application of patient profiling with corresponding phenotype-specific treatment recommendations may be beneficial for implementation of GDMT in real-world clinical practice. However, it is important to mention that, although we were able to study treatment by profile, the data presented are cross-sectional, and it was unknown whether side effects or tolerability may have influenced prescription of HF drugs. Our study may serve as an important platform to expand our knowledge of patient profiling, and may contribute to its use in clinical practice. Future studies investigating the barriers to guideline implementation, and the optimal strategy for drug sequencing based on phenotypic profile are needed to improve our understanding of the titration process in real-world setting and may improve implementation of guideline-recommended pharmacological HF therapy.Nearly forty percent of a real-world chronic HF population was classified in the eleven patient profiles as suggested by the HFA. Phenotype-specific treatment recommendations are clinically relevant and important to further improve guideline implementation.


The DAPA‐CKD (Dapagliflozin and Prevention of Adverse Outcomes in Chronic Kidney Disease) trial (NCT03036150) demonstrated that dapagliflozin reduced the risk of kidney and cardiovascular events in patients with chronic kidney disease and albuminuria with and without type 2 diabetes. We aimed to determine whether baseline cardiovascular medication use modified the dapagliflozin treatment effect.2 and urinary albumin:creatinine ratio 200 to 5000 mg/g to dapagliflozin 10 mg or placebo once daily. The primary end point was a composite of ≥50% estimated glomerular filtration rate decline, end‐stage kidney disease, and kidney or cardiovascular death. Secondary end points included a kidney composite end point (primary composite end point without cardiovascular death), a cardiovascular composite end point , and all‐cause mortality. We categorized patients according to baseline cardiovascular medication use/nonuse. Patients were required by protocol to receive a stable  dose of a renin‐angiotensin‐aldosterone system inhibitor. We observed consistent benefits of dapagliflozin relative to placebo, irrespective of baseline use/nonuse of renin‐angiotensin‐aldosterone system inhibitors (98.1%), calcium channel blockers (50.7%), β‐adrenergic antagonists (39.0%), diuretics (43.7%), and antithrombotic (47.4%), and lipid‐lowering (15.0%) agents. Use of these drugs in combination with dapagliflozin did not increase the number of serious adverse events.We randomized 4304 adults with baseline estimated glomerular filtration rate 25 to 75 mL/min per 1.73 mThe safety profile and efficacy of dapagliflozin on kidney and cardiovascular end points in patients with chronic kidney disease were consistent among patients treated and not treated at baseline with a range of cardiovascular medications.clinicaltrials.gov. Identifier: NCT03036150. AEadverse eventCANVASCanagliflozin Cardiovascular Assessment StudyCKD‐EPIChronic Kidney Disease Epidemiology CollaborationCREDENCECanagliflozin and Renal Events in Diabetes with Established Nephropathy Clinical EvaluationCRTcardiac resynchronization therapyDAPA‐CKDDapagliflozin and Prevention of Adverse Outcomes in Chronic Kidney DiseaseDECLARE‐TIMI 58Dapagliflozin Effect on Cardiovascular Events–Thrombolysis in Myocardial Infarction 58EMPA‐REG(Empagliflozin) Cardiovascular OutcomeEMPEROR‐ReducedEmpagliflozin Outcome Trial in Patients With Chronic Heart Failure With Reduced Ejection FractionFIDELIO‐DKDFinerenone in Reducing Kidney Failure and Disease Progression in Diabetic Kidney DiseaseNCnon calculableRAASrenin‐angiotensin‐aldosterone systemSGLT2sodium‐glucose cotransporter‐2The DAPA‐CKD (Dapagliflozin and Prevention of Adverse Outcomes in Chronic Kidney Disease) trial demonstrated that compared with placebo, dapagliflozin reduced adverse kidney and cardiovascular outcomes and all‐cause mortality in patients with chronic kidney disease, irrespective of the presence of diabetes; however, it is unknown whether the use of cardiovascular medications modifies these effects of dapagliflozin in this patient population.We investigated the benefits of dapagliflozin in participants treated and not treated with a range of cardiovascular medications—diuretics, calcium channel blockers, β‐adrenergic antagonists, antithrombotic agents, and lipid‐lowering agents—used as standard of care in the aforementioned patient population; results showed that the efficacy of dapagliflozin on kidney and cardiovascular end points was consistent, with no change in associated safety, irrespective of treatment with a range of cardiovascular medications.These results document the safety and efficacy of dapagliflozin with use/nonuse of a wide range of cardiovascular medications, demonstrating benefits on kidney and cardiovascular outcomes in patients with mild, moderate, and advanced chronic kidney disease, with and without diabetes, which are populations at higher risk of adverse drug effects relative to persons with normal or near normal kidney function. Furthermore, the demonstrated safety of coadministration of dapagliflozin with a range of antithrombotic and lipid‐lowering agents may allow patients to enjoy the benefits of dapagliflozin on reducing risks of heart failure and progressive kidney disease, while safely gaining treatment benefits with other agents aimed to reduce cardiovascular complications, thus expanding the treatment repertoire for this patient population.In the DAPA‐CKD (Dapagliflozin and Prevention of Adverse outcomes in Chronic Kidney Disease)) trial, the sodium‐glucose cotransporter‐2 (SGLT2) inhibitor dapagliflozin reduced the risk of major kidney and cardiovascular events and prolonged survival in patients with chronic kidney disease (CKD), with and without type 2 diabetes.Patients included in these and other cardiovascular trials are typically treated with a variety of concomitant medications, many of which are indicated either for control of hypertension or for prevention or treatment of other cardiovascular conditions . In DAPA‐CKD and other trials, maximally tolerated doses of renin‐angiotensin‐aldosterone system (RAAS) inhibitors were required by protocol.The DAPA‐CKD trial required that all patients be treated with standard of care  therapies for kidney, cardiovascular, and other conditions unless contraindicated.In this prespecified analysis of the DAPA‐CKD trial, we aimed to examine whether baseline use of cardiovascular medications modified the effects of dapagliflozin on kidney and cardiovascular end points and on safety in patients with CKD and albuminuria.https://astrazenecagrouptrials.pharmacm.com/ST/Submission/Disclosure.Data underlying the findings described in this article may be obtained in accordance with AstraZeneca's data sharing policy described at www.vivli.org. Data for studies not listed on Vivli can be requested through Vivli at https://vivli.org/members/enquiries‐about‐studies‐not‐listed‐on‐the‐vivli‐platform/. AstraZeneca Vivli member page is also available outlining further details: https://vivli.org/ourmember/astrazeneca/.Data for studies directly listed on Vivli can be requested through Vivli at clinicaltrials.gov (NCT03036150). The safety of participants in the trial was overseen by an independent Data and Safety Monitoring Committee. The DAPA‐CKD trial was conducted according to the principles of the Declaration of Helsinki. Ethics committees at all participating centers approved the protocol, and all participants provided written informed consent before any study‐specific procedure commenced.DAPA‐CKD was a randomized, double‐blind, placebo‐controlled, multicenter clinical trial; articles describing trial design, baseline characteristics, primary results, and results stratified by diabetes status, history of cardiovascular disease, and several other baseline clinical characteristics have been previously published.2, and urinary albumin:creatinine ratio 200 to 5000 mg/g were eligible for participation. We required patients to be treated with a stable maximally tolerated dose of RAAS inhibitor (angiotensin‐converting enzyme [ACE] inhibitor or angiotensin receptor blocker [ARB]) for ≥4 weeks, unless medically contraindicated. Key exclusion criteria included documented diagnosis of type 1 diabetes, polycystic kidney disease, lupus nephritis, or antineutrophil cytoplasmic antibody‐associated vasculitis. A complete list of inclusion and exclusion criteria and the trial protocol have been previously published.Adults with or without type 2 diabetes, estimated glomerular filtration rate (eGFR) 25 to 75 mL/min per 1.73 m2 was limited to no more than 10% of trial participants. Participants and all study personnel (except the Independent Data Monitoring Committee) were masked to treatment allocation.Participants were randomly assigned to dapagliflozin 10 mg once daily or matching placebo, in accordance with the sequestered, fixed‐randomization schedule, with the use of balanced blocks to ensure an approximate 1:1 ratio of the 2 regimens. Randomization was stratified by diabetes status and urinary albumin:creatinine ratio (≤or >1000 mg/g). We calculated eGFR using the Chronic Kidney Disease Epidemiology Collaboration and incorporated results from the equation as originally defined, including a term for self‐reported race (Black versus non‐Black race). Recruitment of patients with eGFR 60 to 75 mL/min per 1.73 mAfter randomization, in‐person study visits were performed after 2 weeks, 2, 4, and 8 months, and at 4‐month intervals thereafter. At each follow‐up visit, study personnel recorded vital signs, obtained blood and urine samples, and recorded information on potential study end points, adverse events, concomitant therapies, and study drug adherence.2 confirmed by a second measurement after at least 28 days), or death from a kidney or cardiovascular cause. Secondary end points were time to (1) a composite kidney end point of ≥50% sustained decline in eGFR, kidney failure, or death from kidney disease; (2) a composite cardiovascular end point defined as hospitalization for heart failure or cardiovascular death; and (3) death from any cause. We also prespecified change in eGFR slope as an exploratory efficacy end point. All efficacy end points were adjudicated by a masked, independent Clinical Events Committee, except for the quantitative assessments of eGFR, which were obtained from our central laboratory.The primary composite end point was time to ≥50% decline in eGFR (confirmed by a second serum creatinine measurement after at least 28 days), onset of kidney failure , calcium channel blockers, β‐adrenergic antagonists, antithrombotic agents (antiplatelet drugs or anticoagulants), and lipid‐lowering agents (statins or other). We further categorized patients according to baseline RAAS inhibitor dose, defined as 100% (or more), 50 to <100%, and <50% of the maximum labeled antihypertensive dose, and those not treated with RAAS inhibitors.P values <0.05 to be statistically significant.The overall analytic approach, power calculation, and prespecified statistical analysis plan have been previously published.2 and urinary albumin:creatinine ratio of 200 to 5000 mg/g were randomized to either dapagliflozin 10 mg or placebo once daily with a median follow‐up of 2.4 years. Table As previously described,Cardiovascular medications were generally used more frequently in patients with diabetes, but there were no material differences in the proportion of patients using cardiovascular medications by randomized group. Of the 4304 randomized participants, 4224 (98.1%) were receiving a RAAS inhibitor at baseline, with roughly twice as many patients treated with ARBs as compared with ACE inhibitors. Among the 4296 (99.9%) participants with available data on baseline RAAS inhibitor dose, 1231 (28.6%), 1867 (43.5%), and 1068 (24.8%) were prescribed 100% (or more), 50 to <100%, and <50% of the maximum labeled antihypertensive dose, respectively, and 130 (3.0%) were not prescribed RAAS inhibitors.Figure Among patients randomized to placebo, event rates for the primary composite end point were highest among the 3.0% of participants untreated with RAAS inhibitors (13.2 events per 100 patient‐years); event rates were similar among the 3 other RAAS inhibitor dose groups . Dapagliflozin compared with placebo consistently reduced the risk of the primary and secondary end points irrespective of the RAAS inhibitor dose group .The effects of dapagliflozin (versus placebo) on attenuating the decline in eGFR was evident irrespective of the dose of the prescribed RAAS inhibitor dose  and those treated and not treated with thiazide diuretics (interaction P=0.46).Table We conducted this prespecified analysis to determine whether a variety of commonly prescribed cardiovascular medications modified the safety and efficacy of dapagliflozin in patients with CKD, with and without type 2 diabetes, a sizeable fraction of whom have underlying cardiovascular disease, including coronary, cerebral and peripheral arterial disease, heart failure, atrial fibrillation, and hypercholesterolemia. We found consistent benefits of dapagliflozin on the primary composite end point, as well as on the secondary end points, among patients treated and not treated with diuretics , calcium channel blockers, β‐adrenergic antagonists, antithrombotic agents (antiplatelet agents or anticoagulants), and lipid‐lowering medications. Moreover, we found no evidence that use of any of these medication classes modified the safety profile of dapagliflozin.In addition to examining whether cardiovascular medications modified the effects of dapagliflozin in this population, we took the opportunity to examine the effects of dapagliflozin in patients based on baseline dosing of ACE inhibitors or ARBs. While higher doses of ACE inhibitors or ARBs are generally thought to afford patients a greater benefit than lower doses,Our results generally confirm findings observed in other populations treated with dapagliflozin and in patients with diabetic kidney disease treated with dapagliflozin and other SGLT2 inhibitors. In the DAPA‐HF trial, in which the primary composite end point was cardiovascular death or worsening heart failure, Docherty et al found consistent benefits of dapagliflozin by use/nonuse or dose of a variety of background heart failure treatments, including diuretics, RAAS inhibitors, and β‐adrenergic antagonists in patients with heart failure with reduced ejection fraction.While the effects of antithrombotic and lipid‐lowering agents on kidney function are not as profound as those of other cardiovascular drugs that alter renal hemodynamics, including diuretics, RAAS inhibitors, β‐adrenergic antagonists, and calcium channel blockers, antiplatelet agents, anticoagulants, statins, and other lipid‐lowering agents are commonly prescribed in patients with CKD, particularly those with type 2 diabetes, a history of cardiovascular events, and atrial fibrillation. The demonstrated safety of coadministration of dapagliflozin with a range of antithrombotic and lipid‐lowering agents should allow patients to enjoy the benefits of dapagliflozin on reducing risks of heart failure and progressive kidney disease, while safely deriving the benefits of treatment with other agents aimed to reduce complications of atherosclerotic vascular disease and to prevent ischemic and cardioembolic stroke.RAAS inhibitors were initially shown to slow the progression of CKD more than 2 decades before publication of the CREDENCE and DAPA‐CKD trial results.While the bulk of the analyses presented here address efficacy end points, the safety findings are equally if not more meaningful. Despite theoretical risks associated with coadministration of SGLT2 inhibitors and diuretics, RAAS inhibitors, β‐adrenergic antagonists, and calcium channel blockers, we saw no increase in serious adverse events or adverse events of special interest when these agents were used in combination with dapagliflozin, or when several of these agents were used in combination. Indeed, serious adverse events were less frequent among patients treated with dapagliflozin compared with placebo.There are several limitations to these analyses. First, while we can definitively state that there was no evidence of heterogeneity of the dapagliflozin effect depending on use/nonuse of cardiovascular medications or in the case of RAAS inhibitors, the proportion of maximum labeled antihypertensive dose at baseline, the power to detect interactions was limited, and we may have missed subtle but clinically meaningful accentuation or attenuation of effects that might be evident in larger patient samples. It may be informative to repeat these analyses using population‐based  samples in future years. Second, given differences in practice patterns among and within geographic regions, we were unable to examine effect modification by individual agents, as the modest sample size of the trial required us to explore these interactions by drug class. Finally, although the broad inclusion criteria for the DAPA‐CKD trial resulted in inclusion of participants with many underlying causes of CKD, we had insufficient power to determine whether any cardiovascular drug class or individual agent exerted unique modifying effects in patients with immunoglobulin A nephropathy, focal segmental glomerulosclerosis, membranous nephropathy, or other specific causes of CKD.In summary, the safety profile and efficacy of dapagliflozin on kidney and cardiovascular end points in patients with CKD were consistent among patients treated and not treated with a range of cardiovascular medications.This study was funded by AstraZeneca. The funder of the study was involved in the study design, data analysis, data interpretation, writing of the report, and the decision to submit the paper for publication.Heart.org, ProAdWise Communications, Radcliffe Cardiology, Servier, and the Corpus. Dr Rossing has received fees to Steno Diabetes Center Copenhagen for: steering group membership and/or lectures and advice from AstraZeneca, Novo Nordisk, Bayer, and Eli Lilly; advisory board participation from Sanofi Aventis and Boehringer Ingelheim; and steering group participation from Gilead. Dr Langkilde and Dr Sjöström are employees and stockholders of AstraZeneca. Dr Toto is a member of the Executive Committee of the DAPA‐CKD study, has received consulting fees from Boehringer Ingelheim, Reata Pharma, and Chinook, and payment from Medscape and Medical Education Resources, participated in advisory boards for Bayer and Vioforand on data monitoring committees for Akebia and Otsuka.Dr Chertow has received fees from AstraZeneca for service on the DAPA‐CKD trial steering committee. He serves on the Board of Directors for Satellite Healthcare. He has served on other trial steering committees for Akebia, AstraZeneca, Gilead, Sanifit, and Vertex, and on data safety monitoring boards for Bayer, Mineralys, and ReCor. He has served as an advisor and received fees and/or stock options from Ardelyx, CloudCath, Durect, Miromatrix, Outset, and Unicycive. He has received research grants from the National Institute of Diabetes and Digestive and Kidney Diseases, National Heart, Lung, and Blood Institute, and National Institute of Allergy and Infectious Diseases. Dr Correa‐Rotter has received fees from AbbVie, AstraZeneca, GlaxoSmithKline, Medtronic, and Boehringer Ingelheim, and has lectured for Amgen, Janssen, Takeda, AstraZeneca, and Boehringer Ingelheim and has received research support from GlaxoSmithKline, Novo Nordisk, and AstraZeneca. P. Vart reports receiving a travel grant from AstraZeneca. Dr McMurray has received payments to his employer, Glasgow University, for his work on clinical trials, consulting, and other activities from AstraZeneca, Cytokinetics, KBP Biosciences, Amgen, Bayer, Theracos, Ionis Pharmaceuticals, Dalcor Pharmaceuticals, Novartis, GlaxoSmithKline, Bristol Myers Squibb, Boehringer Ingelheim, Cardurion, and Alnylam, and has received personal lecture fees from Abbott, Alkem Metabolics, Eris Life Sciences, Hickma, Lupin, Sun Pharmaceuticals, Medscape/Dr Wheeler has received consultancy fees from AstraZeneca and personal fees from Amgen, Astellas, Bayer, Boehringer Ingelheim, Gilead, GlaxoSmithKline, Janssen, Napp, Mundipharma, Reata, Tricida, Vifor Fresenius, Merck Sharp and Dohme, ProKidney, Galderma, and Zydus. H.J.L. Heerspink has received honoraria  for participation in steering committees from AstraZeneca, Janssen, Gilead, Bayer, Chinook, and CSL Pharma; honoraria for participation in advisory boards from Merck, Mitsubishi Tanabe, Janssen, and Mundipharma; fees for consultancy from AstraZeneca, AbbVie, Retrophin, Boehringer Ingelheim, and Novo Nordisk; and research grant support from AstraZeneca, AbbVie, Janssen, and Boehringer Ingelheim. The remaining authors have no disclosures to report.Data S1:Click here for additional data file.


Rates of late allograft loss have improved slowly in the last decades. Well described traditional risk factors that influence allograft survival include cardiovascular events, rejection, infections and post-transplant neoplasia. Here, we critically evaluate the influence of several non-immunological, non-traditional risk factors and describe their impact on allograft survival and cardiovascular health of kidney transplant recipients. We assessed the following risk factors: arterial stiffness, persistent arteriovenous access, mineral bone disease, immunosuppressive drugs residual levels variability, hypomagnesemia, glomerular pathological alterations not included in Banff criteria, persistent inflammation and metabolic acidosis. Recent high-level evidence confirms the superiority of transplantation in reducing all-cause mortality for most patients with end-stage kidney disease . In the We searched three databases: Medline, EMBASE and Cochrane Library, and included studies from January 2000 to December 2022 with the following terms: “kidney graft survival”, “kidney graft loss”, “failed kidney transplant”, “chronic allograft nephropathy”, “risk factors”, “chronic kidney disease progression”.Late allograft failure is a heterogenous background picture of several multifactorial pathogenic entities that all contribute to kidney dysfunction in transplant patients. Naesens et al. elegantly described data derived from failing grafts: Importantly, one quarter of biopsies exhibit more than one coexisting disease, and almost one third of patients with lost allograft had no specific diagnosis on the last biopsy performed but highlighted the important prognostic capacity of the extent of chronic damage . ConsequRecent efforts have focused on better characterization of allograft loss and described a more complex picture of the multifactorial pathogenic processes. A study conducted by Mayrdorfer et al. examining 303 death-censored graft losses among 1642 transplant recipients discriminated primary failure causes (responsible for more than 50% persistent estimated glomerular filtration rate decrease) from secondary failure causes (contribution to less than 50% estimated glomerular filtration rate decline) and analyzed data in a time-dependent manner. Late allograft failure was mainly attributed to intercurrent medical events, defined as cardiovascular events or infections conducting to prominent decline in renal function, and representing almost 40% of late graft failures. Moreover, antibody-mediated rejection met increasing frequencies with time as a primary failure cause (48.6% of graft failures at more than 10 years from transplant intervention) while T-cell mediated rejection had a sharp decline with transplant vintage. Interestingly, calcineurin-inhibitors toxicity was isolated in only two cases as a primary cause, but it was described as the most frequent secondary cause responsible for graft loss (64 of 240 late graft failures). The authors once again highlighted the multifactorial etiology of transplant failure .The ability to predict the evolution of the transplanted kidney is of great significance and has been the center of some valuable research. Search of prognostic factors begins even before implantation with evaluation of donor quality. Preimplantation histology may provide important clues but clinical decisions of implanting or discarding the kidney based on this data have shown conflicting results. In a recent large multinational study, discarded kidneys due to biopsy alterations could have been offered to patients with improved survival and quality of life. Data shows 70% of implanted similar grafts matched with the refused grafts due to biopsy were functioning at 10 years .Prediction models can be computed by means of artificial intelligence and deep learning models to predict evolution of estimated glomerular filtration rate after kidney transplantation. Raynaud et al. identified, after performing multinomial regression models, seven significant determinants of progression: donor age, estimated glomerular filtration rate, proteinuria, graft scarring, graft interstitial inflammation and tubulitis, microcirculation inflammation and circulating anti-HLA donor specific antibodies. Resulting computed trajectories were significantly associated with progression to allograft loss .Other techniques proved good forecast capability utilizing sequence-to-sequence modelling in order to create a machine learning algorithm-based prediction model that is based on inputting repeated estimated glomerular filtration rate measurements .Recently, an interesting dynamic prediction tool that incorporates histological, immunological and clinical parameters together with repeated measurements of graft function was validated in multiple cohorts and proved superior prediction performance of allograft survival compared to other similar systems. Individualized parameters that altered survival were estimated glomerular filtration rate, proteinuria, delay between transplant and evaluation, Banff categories , mean fluorescence intensity of anti-HLA donor specific antibodies and repeated measurements of eGFR. The iBox system has proved good prediction ability of graft survival and it has been validated as a valuable endpoint in clinical trials in transplantation ,15.Cardiovascular disease has recently gained well-deserved attention since it represents the main driver of comorbidity and mortality in kidney transplant recipients. Pregnant emphasis was attributed to defining and addressing traditional cardiovascular risk factors in improving survival outcomes in this population. Management of these associated relevant comorbidities have been reviewed elsewhere and they do not meet the purpose of this paper  16,17].,17.16,17The association between increased values of arterial stiffness and eGFR decline has been noted in the general chronic kidney disease (CKD) population and suggests that the resulting hemodynamic stress insults the kidney by means of endothelial dysfunction and microvascular ischemia. Chronic inflammation, oxidative stress and overactivation of the renin–angiotensin systems may represent other possible mechanistic underpinnings . Aortic Continuous functioning arteriovenous fistula after kidney transplantation has the potential to worsen the maladaptive remodeling changes in the cardiovascular system and alter kidney function. A meta-analysis addressing the impact of ligating the arteriovenous fistula showed that patients undergoing access occlusion had better cardiac morphological parameters and better kidney allograft function .Furthermore, patency at 5 years was only 61% and arteriovenous fistula-related complications were frequently reaching about 30% of patients. On the other hand, preservation of the vascular access may provide the advantage of an optimal backup strategy if transition to dialysis is needed. Consequently, decisions should be individualized on several parameters, mainly based on renal and cardiac functions and on selecting complications-prone profile of patients .After kidney transplantation, particularly older patients find themselves at an increased risk for pathological fractures. The fractures appear multifactorial in nature  and are associated with unfavorable outcomes, such as subsequent graft loss or mortality. It remains unclear whether only the high end of mineral bone disease spectrum (fractures) is linked to allograft survival decline or if other biologic entities contribute as well .Persistent mineral abnormalities after kidney transplantation mainly include hypercalcemia and hypophosphatemia. Previous severe hyperparathyroidism and high levels of circulating PTH and FGF-23 are established risk factors for altered mineral metabolism post-transplantation. However, impact on outcomes is unclear. As they may not contribute directly to allograft loss and rapid eGFR decline, they are the main drivers of vascular calcifications affecting graft vessels and, interestingly, of tubulointerstitial calcifications on allograft biopsies ,31.Cinacalcet has proven its ability to correct persistent hyperparathyroidism after kidney transplantation and the associated metabolic derangements but without impact on bone mineral density or allograft survival . Data frBiphosphonate therapy in kidney transplant recipients has the clear benefit of improving bone mineral density. Recent studies have proven low rates of adynamic bone disease, strengthening the safety profile of this medication. However, there is no clear data on rates of fractures and there are no benefits or harms related to renal function evolution in kidney transplant recipients .Assessment of pre-transplantation profile of mineral bone disease phenotype may predict survival and high-risk patients. In a cohort of more than 11,000 patients, recipients that exhibited higher levels of pre-transplant serum alkaline phosphatase (>120/U/L) were subject to increased post-transplant mortality. No association was found between circulating PTH or calcium and post-transplant evolution .The negative correlation between graft survival and vitamin D deficiency has been described in an observational manner . InsuffiCurrent therapeutic armamentarium provides means to effectively correct mineral abnormalities in kidney transplant recipients. However, impact of described treatments on graft survival have been insufficiently studied and proved no significant benefits.Excessive immunosuppression contributes to significant complications linked to allograft loss; infections, malignancy and polyoma virus nephropathy account for about one third of graft losses . BecauseInterestingly, another tool available for the evaluation of drug exposure is the time in therapeutic range—addresses the interval of time that a patient is exposed to correct predefined range of values of immunosuppression. Patients with high variability and time in therapeutic range < 40% were at increased risk of de novo donor-specific antibodies and at an estimated four times greater risk of graft loss by 5 years .Hypomagnesemia represents one of the most frequent electrolyte disturbances post-transplantation . SeveralMagnesium deficit exhibits diabetogenic effects and it represents a proven independent risk factor for post-transplant diabetes mellitus ,49. ReceReports on mortality are conflicting and derived from observational retrospective studies. There have been signals of the possible interaction between hypomagnesemia and graft dysfunction . RecentlMagnesium metabolism, however, is important and may have implications in long-term outcomes as it interferes with glycemic alterations, endothelial dysfunction and cardiovascular abnormalities. In addition, correction is cumbersome and magnesium levels may not reflect true stores. No significant trials regarding supplementation and the associated impact on graft function have been conducted .Overall, due to conflicting results and the presence of many confounders, such as the use of CNI, nutritional status, measurement issues, other additional studies addressing this problem will evaluate true impact.The kidney transplant biopsy interpretation is submitted to a well described and standardized score system—Banff classification. Diagnostic entities are established by pathologists after grading several acute and chronic lesions . HoweverDenic et al. conducted a study with repeated biopsies in well-functioning transplant recipients at 5 years post-implantation. Three parameters previously linked to unfavorable graft outcomes were examined: glomerular volume, global glomerulosclerosis and ischemic appearance of glomeruli. All three parameters combine successfully predicted death-censored graft failure at 5 years. Moreover, glomerular ischemia is significantly associated with allograft loss, even after adjustment for Banff scores .Moreover, other recent data highlight the impact of early subclinical inflammation detected on surveillance biopsies on allograft survival. Patients with subclinical inflammation and tubulitis were exposed to a higher hazard of acute rejection and death-censored allograft loss .Implantation of the kidney allograft is often realized in uremic patients exhibiting a state of inflammatory milieu. Moreover, inflammation is enhanced by perioperative events and induced by other stressors. For example, brain death is linked to increased cytokine excretion and ischemia-reperfusion injury can lead to delayed graft function. All forms of rejection are responsible for elevated markers of inflammation, and viral infections could be a source of enhanced cytokine production leading to overreactive innate immunity . AtherosThe persistent inflammatory state is multifactorial and contributes to graft dysfunction by several mechanisms: triggering chronic hypoxia, extracellular matrix deposition and extending graft fibrosis . FurtherMetabolic acidosis identifies as one of the most common complications in kidney transplant recipients. The fall of bicarbonate and pH levels with a decrease in serum partial pressure of carbonic dioxide occurs mainly when the glomerular filtration rate falls under 30 mL/min in patients with chronic kidney disease. This threshold is higher in kidney transplant recipients, showing that metabolic acidosis may occur at higher GFRs in these patients ,64. The Data on allograft survival are pointing to metabolic acidosis as a risk factor for graft dysfunction in the long term. Park et al. conducted a study that showed that low TCO2 concentrations 3 months after transplant are associated with an increased risk of graft loss and death-censored graft failure . FurtherThus, these associations require the application of therapeutic measures to correct acidosis. From this point of view, the data are limited, with most studies focusing on alkali therapy. If supplementation with sodium bicarbonate is effective in patients witch chronic kidney disease, the data in kidney transplant recipients are limited. Schulte at al. evaluated the effect of sodium bicarbonate in kidney transplant recipients with chronic metabolic acidosis. Treatment with sodium bicarbonate was associated with an increased risk of graft failure with no changes in mortality. Authors speculated that the interference with gastric acid pH of oral bicarbonate intake may lead to altered absorption of mycophenolate mofetil. Consequently, patients may be exposed to lower plasma concentrations of mycophenolic acid . The recLong-term survival of kidney allografts is altered mainly by cardiovascular disease, malignancy and infections. However, in order to maximize survival, other relevant pathogenic entities need to be addressed. Care of kidney transplant patients is complex and needs to be centered also on many potential risk factors that can influence long-term survival, consisting of the following: metabolic derangements such as hypomagnesemia, metabolic acidosis and mineral bone disease after transplantation, cardiovascular disease manifesting as arterial stiffness, immunosuppressive drug metabolism and non-adherence, persistent inflammation and glomerular changes not highlighted by Banff criteria. .


The prevalence of atrial fibrillation (AF) is increasing as the elderly population continues to increase. Chronic kidney disease, diabetes, and hypertension are known risk factors for AF. Since multimorbidity exists in chronic kidney disease, it is difficult to determine the impact of hypertension alone. Furthermore, little is known about the impact of hypertension on predicting AF in diabetic end-stage renal disease (ESRD). Here, we evaluated the effect of differential blood pressure control on AF prevalence among the diabetic ESRD population.From the Korean National Health Insurance Service database, 2 717 072 individuals with diabetes underwent health examinations during 2005–2019. Exactly 13 859 individuals with diabetic ESRD without a prior history of AF were selected and included in the analysis. Based on blood pressure level and previous hypertension medication history, we subdivided them into five groups: normal (normotensive), pre-hypertension, new onset hypertension, controlled hypertension, and uncontrolled hypertension. AF risk according to the blood pressure groups was estimated using Cox proportional-hazards models.Among the five groups, the new onset hypertension, controlled hypertension, and uncontrolled hypertension groups showed a higher AF risk. In patients on antihypertensives, diastolic blood pressure ≥100 mmHg was significantly associated with AF risk. High pulse pressure showed a significant risk for AF in patients on antihypertensives.In patients with diabetic ESRD, overt hypertension and a history of hypertension impacts AF. AF risk was higher in the ESRD population with diastolic blood pressure ≥100 mmHg and pulse pressure >60 mmHg. Atrial fibrillation (AF) is the most common arrhythmia diagnosed in adults. It tends to increase in prevalence as an aging population increases, and medical expenses simultaneously increase as a result . Due to Although ESRD is a known risk factor for AF, we attempted to find the major factors involved in the development of AF in these patients. To compare the risk of AF by BP status, we decided to analyze diabetic patients with ESRD. This study aimed to elucidate the impact of BP on developing AF in diabetes patients with ESRD and to further provide some guidelines for BP management in patients with ESRD to lower AF development.International Classification of Disease, Tenth Revision of Clinical Modification. Korean National Health Insurance Service data also cover inpatient and outpatient clinic services, medical costs, pharmacy claims, and mortality information. Since the National Health Insurance Corporation recommends and provides a national general medical examination for the early detection of chronic diseases for adults over the age of 20 years, this examination includes physical examinations by a primary physician, blood tests, chest radiography, and self-reporting questionnaires regarding social and medical history. These results are contained in the National Health Insurance Service database and were provided anonymously. This study was conducted according to the Declaration of Helsinki and approved by the Institutional Review Board of St. Vincent’s Hospital, The Catholic University of Korea . Regarding the retrospective nature of the data, informed consent was waived, which was also approved by the Institutional Review Board of St. Vincent’s Hospital, The Catholic University of Korea.The Korean National Health Insurance Service database (from 2005 to 2019) was used for this study. The Korean National Health Insurance Service is a single national insurer and includes comprehensive information with overall medical coverage. This service has approximately 50 million individuals enrolled from the Republic of Korea, and data from this population include sociodemographic data, medical expenses, and diagnoses encoded by the International Classification of Disease, Tenth Revision of Clinical Modification. Body mass index (BMI) was calculated as a person’s weight in kilograms divided by the square of height in meters. In the relevant diabetic population (n = 2 717 072), we selected 15 909 patients with ESRD based on eGFR <15 mL/min/1.73 m2 and excluded 1366 patients with previous AF history and 684 who followed up less than 1 year later. Overall, 13 859 ESRD patients with diabetes were included in the analysis .Continuous variables are described as means ± standard deviations, and categorical variables are described as numbers and percentages. Across the five groups, baseline characteristics were analyzed. The incidence of newly developed AF was analyzed by dividing the number of events during the follow-up period by 1000 person-years at risk. The AF risk in relation to the hypertension levels was analyzed via survival analysis, using the log-rank test and Kaplan-Meier method. The hazard ratio (HR) and 95% confidence intervals (CIs) for AF were analyzed by Cox proportional-hazards regression. The non-adjusted analysis was presented as Model 1, and after adjustment for age and sex, it was presented as Model 2. After adjusting for age, sex, BMI, dyslipidemia, and smoking/alcohol consumption, it was presented as Model 3. Model 4 was further adjusted for age, sex, BMI, smoking, alcohol consumption, insulin treatment, number (≥3) of antidiabetic medications used, and duration (≥5 years) of diabetes condition. Subgroup analyses for SBP, DBP, and PP were conducted, and patients with or without antihypertensive medications were also analyzed. All the analyses were two-tailed, and The average ages of patients in the normal, pre-hypertension, new onset hypertension, controlled hypertension, and uncontrolled hypertension groups were 56.7 ± 11.4 (n = 880), 57.1 ± 11.2 (n = 1535), 58.4 ± 11.8 (n = 740), 60.7 ± 10.7 (n = 6383), and 61.4 ± 10.4 years (n = 4321), respectively. More than 50% of the study population was male. The percentages of individuals with diabetes conditions for more than 5 years in the normal, pre-hypertension, new onset hypertension, controlled hypertension, and uncontrolled hypertension groups were 51.7%, 49.0%, 52.7%, 65.0%, and 71.0%, respectively. Overall, 75% of the patients were on hemodialysis. Compared with the normal and pre-hypertensive groups, the other three groups showed higher BMI, higher waist circumferences, a high proportion of insulin treatment, and lower high-density lipoprotein levels. Baseline characteristics of the study population according to the five groups are shown in The trend of AF risk in each group was: Normal group < Pre-hypertension group < New onset hypertension group < Controlled hypertension group < Uncontrolled hypertension group . In ModeA multivariate analysis according to the constituent factors of BP was performed. After adjusting for age, sex, BMI, smoking, drinking, insulin treatment, higher number (≥ 3) of antidiabetic medications used, and duration of diabetes (≥ 5 years), higher SBP showed a trend of increased AF risk without statistical significance. DBP > 100 mmHg (HR 1.26 [1.054–1.505]), PP between 60 and 80 mmHg (HR 1.359 [1.024–1.805]), and PP ≥ 80 mmHg (HR 1.601 [1.211–2.117]) were associated with an increased risk of AF .In addition, AF risk was evaluated according to antihypertensive medication use. In patients on antihypertensive medications, DBP ≥ 100 mmHg was significantly associated with AF risk (HR 2.072 [1.195–3.591]). In patients on antihypertensive medication, high PP was significantly associated with AF risk  .An increase in BP can accelerate a decrease in renal function, and the retention of water and salt also causes a further increase in BP. Further, endothelial dysfunction and arterial stiffness are common characteristics of diabetes and ESRD, which can cause diverse cardiovascular diseases, including AF . These mIn the ACCORD study, an intensive BP control group lowered the incidence of AF. In that study, the intensive BP control target was SBP < 120 mmHg . HoweverAlthough current guidelines for hypertension management suggest upper limits for both SBP and DBP, DBP is usually overlooked in clinics. It is reported that even in controlled hypertension, lower than normal or higher than normal DBP is associated with higher cardiovascular mortality and stroke , 21. In PP, by definition, is affected by SBP and DBP. It is well known that as individuals age, PP increases due to arterial stiffening. Similar to SBP, PP exhibits a J-curve showing poor prognosis at lower and higher PP. Low PP particularly is a poor prognostic factor in both the elderly and patients with heart failure . High PPCurrently, with the development of novel oral anticoagulants (NOACs) and tremendous progress in ablation techniques, it has become possible to systematically manage and treat AF more effectively than in previous years. Optimizing treatment for the underlying disease must be prioritized as much as anticoagulant treatment for already diagnosed AF and rhythm control to restore or to maintain sinus rhythm. This is called upstream therapy. For example, optimized glycemic control and managing diabetic complications are required in patients with DM, and optimized BP management according to guidelines is also required in patients with high BP. Since various conditions may affect temporal BP, such as during hemodialysis or autonomic dysfunction with fluctuating BP according to medical treatment, there is a lack of consensus regarding the optimal BP target in CRF, especially in ESRD. In a study by Li et al. , higher Nonetheless, in our study, the new onset hypertension, controlled hypertension, and uncontrolled hypertension groups showed higher AF risk than the normotensive and pre-hypertension groups. This is interesting because even in patients with controlled hypertension (those on antihypertensive medication), the risk of AF was higher than in patients with normotension or pre-hypertension. The high risk of AF despite the BP being controlled with treatment suggests that prevention at an early stage of hypertension is more important. This, therefore, shows the importance of upstream treatment.AF risk was especially higher in treated patients with PP > 60 mmHg. In patients without treatment, diastolic hypertension (≥ 100 mmHg) showed a higher AF risk. Based on the results of this study, the optimal BP target in ESRD to lower AF risk is suggested as 120 mmHg ≤ SBP < 140 mmHg and 80 mmHg ≤ DBP < 90 mmHg with PP < 60 mmHg .The Korean National Health Insurance Service is a unique, well-controlled and defined system. Individuals included in this system are recommended to undergo a general medical examination annually or biennially. Based on that dataset, we defined the BP status and medical history of enrolled patients and divided them into five BP levels. Nonetheless, this study has some limitations. First, although this was a large observational study, the setting was significantly different from that of a randomized controlled trial. Despite adjusting for various covariates, there may have been overlooked or unmeasurable confounders. Second, there was an inevitable limitation to the ascertainment of AF using the diagnostic codes rather than the electrocardiogram of each subject due to the nature of the data, although the definition of new-onset AF in the nationwide claims database had been validated and was generally used. Third, the measurement of BP during the health checkup might have been inaccurate due to a lack of repeat measurements and white coat hypertension. It could have been difficult to accurately determine a clinical problem from a faulty blood pressure measurement. Finally, the treatment differences in hypertension management may be meaningful in understanding the relationship between BP levels and outcomes. Although higher daytime BP or a history of hypertension are risk factors for AF, by the nature of these observational studies, treatment for hypertension was not randomly assigned or strictly controlled for the types of antihypertensive medications used or the duration of treatment. Therefore, in this study, attention should have been paid to the interpretation of the treatment effect in hypertensive patients. Future studies evaluating the effect of diurnal variation of BP on AF development and AF risk according to the duration of treatment or types of antihypertensive medications used in patients with ESRD are also needed.In conclusion, untreated hypertension and a history of hypertension in patients with diabetic ESRD increase the risk of AF development. Controlled hypertension also appears to increase AF risk. AF risk was higher in the ESRD population with DBP > 100 mmHg and PP > 60 mmHg. Moreover, in patients using antihypertensive medications, PP ≥ 60 mmHg was a risk factor for AF. As DBP, SBP, and PP affect the occurrence of AF in patients with ESRD, DBP, SBP, and PP should also be emphasized respectively in the treatment target when monitoring and treating hypertension.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.


Obstructive sleep apnea (OSA) severity can vary markedly from night-to-night. However, the impact of night-to-night variability in OSA severity on key cardiovascular outcomes such as hypertension is unknown. Thus, the primary aim of this study is to determine the effects of night-to-night variability in OSA severity on hypertension likelihood. This study uses in-home monitoring of 15,526 adults with ~180 nights per participant with an under-mattress sleep sensor device, plus ~30 repeat blood pressure measures. OSA severity is defined from the mean estimated apnea–hypopnoea index (AHI) over the ~6-month recording period for each participant. Night-to-night variability in severity is determined from the standard deviation of the estimated AHI across recording nights. Uncontrolled hypertension is defined as mean systolic blood pressure ≥140 mmHg and/or mean diastolic blood pressure ≥90 mmHg. Regression analyses are performed adjusted for age, sex, and body mass index. A total of 12,287 participants  are included in the analyses. Participants in the highest night-to-night variability quartile within each OSA severity category, have a 50–70% increase in uncontrolled hypertension likelihood versus the lowest variability quartile, independent of OSA severity. This study demonstrates that high night-to-night variability in OSA severity is a predictor of uncontrolled hypertension, independent of OSA severity. These findings have important implications for the identification of which OSA patients are most at risk of cardiovascular harm. Untreated OSA is associated with a wide range of adverse health and safety consequences including increased risk of hypertension3, cardiovascular disease4, depression5, reduced quality of life6, traffic accidents7, and all-cause mortality4.Obstructive sleep apnea (OSA) is a common clinical sleep disorder characterized by repetitive upper airway collapse during sleep. OSA is estimated to affect approximately one billion people globally9. This has raised concerns regarding OSA misdiagnosis and possible misdirected management and care9. Night-to-night variation in OSA may explain, at least in part, why a single-night diagnosis of OSA shows inconsistent relationships with important health outcomes and responses to treatment12. Indeed, emerging evidence indicates that high night-to-night variability in OSA severity may be an important contributor to cardiovascular diseases such as atrial fibrillation14. Similarly, blood pressure varies widely from day-to-day in some people, and high day-to-day variability in blood pressure is associated with atrial fibrillation, OSA, all-cause mortality, and cardiovascular events, independent of mean blood pressure21. Whether night-to-night changes in OSA severity contribute to blood pressure variability is unknown.Recent evidence indicates that there is considerable night-to-night variation in OSA severity23 and a clinically validated home-blood pressure monitor. We hypothesized that high variability in OSA severity would be a strong predictor of hypertension and blood pressure variability.To investigate the potential clinical importance of night-to-night variation in OSA severity on cardiovascular risk, this study was designed to determine the potential association between night-to-night variability in OSA severity and blood pressure using a validated under-mattress sleep monitorThis study includes data from 12,287 adults monitored over ~180 nights using an under-mattress sleep sensor device accompanied by ~30 repeat blood pressure measurements. Here we use the under-mattress sleep sensor technology to estimate OSA severity  and OSA variability (standard deviation of AHI over the recording period). People in the highest variability quartile within each OSA severity category have a 50–70% increased likelihood of uncontrolled hypertension compared to the lowest variability quartile, regardless of OSA severity. We conclude that high variability in OSA severity is an independent predictor of uncontrolled hypertension and this has major implications for identification of patients most at risk of cardiovascular harm. Furthermore, this study highlights the unique and important insights that multi-night, in-home, non-invasive monitoring of OSA severity can yield.2. However, the proportion of males was slightly higher, and mean BMI and AHI values were ~1 kg/m2 and 3 events/h higher, respectively, in the current study sample  and 1482 (9.5 %) were excluded because they had <28 nights of sleep recordings or an average use of <4 times a week, respectively. A further 346 (2.2%) were excluded due to missing body mass index (BMI) data and 34 (0.2%) as they were <18 or >90 years old. The characteristics of the remaining 12,287 users are summarized in Table p-values < 0.001) for participants with severe and variable OSA compared to controls and those with low night-to-night variability in OSA severity . When OSA severity and variability were combined into one model, the variance inflation factors (a measure of collinearity) for OSA severity and variability were below 5 (2.81 and 2.74), supporting the combination of both metrics into a single model. The likelihood ratio test indicated that a model with both OSA severity and variability provided a superior model fit than a model with OSA severity alone . In this combined model, OSA severity was associated with a 7% increase in uncontrolled hypertension likelihood . Furthermore, OSA variability independent of OSA severity was associated with a 51% increase  in uncontrolled hypertension likelihood.OSA severity  and higher variability in OSA severity  were associated with increased uncontrolled hypertension likelihood in separate models after controlling for age, BMI, and sex. Both associations were nonlinear, and marginal probability plots are shown in Fig. Categorical analysis of OSA severity confirmed that mild, moderate, and severe OSA were associated with increased uncontrolled hypertension likelihood compared to the no OSA group Fig. . Particip = 0.029). Quartile analysis of AHI variability within the OSA severity category also suggested increased hypertension risk for participants with variable AHI, although some results became non-significant likely due to the smaller sample size  participants remained for the sensitivity analysis. Of these, 1170 (21.7%) cases of hypertension were observed. In separate models, high average AHI  and high AHI variability  were associated with an increased risk of hypertension. In the combined model, average AHI was associated with a 14% increase in hypertension risk , and variability in AHI was associated with a 36% increase in hypertension risk . Model fit was superior in the combined model than in the AHI-mean-only model  and a ~0.37 mmHg increases in systolic blood pressure variability  independent of mean AHI. In the combined model, mean AHI was not associated with blood pressure variability. In separate models, mean AHI  and AHI variability  were associated with increased blood pressure variability. The combined model provided a superior model fit compared to a model with mean AHI alone . Quartile analyses also indicated an association between OSA severity categories, OSA variability, and greater blood pressure variability. Severe OSA was associated with ~1 mmHg increase in systolic and ~0.7 mmHg increase in diastolic blood pressure variability compared to no OSA  that may contribute to hypertension risk and blood pressure variability requires further consideration. However, validation studies versus gold standard polysomnography in over 150 participants28. However, the main study findings remained in sensitivity analyses when limited to morning blood pressure entries. Accordingly, circadian influences appear unlikely to have been a major confounder in the current study. Indeed, consistent with previous findings, variability in systolic blood pressure is a significant predictor of death when blood pressure is measured in the morning or evening, or both16. The number of clinical covariates available in this study was also somewhat limited. Thus, the potential impact of uncontrolled confounding behavioral and lifestyle factors 37 and treatment status to influence the current findings remain to be investigated. Like many digital health innovations and consumer-data research, there is a balance between data volume and the ability to capture all clinically relevant variables. Conceptually, these data could also be synchronized via electronic health records databases, and via the development of new digital health tools to automatically capture more diverse aspects of a person’s health. Multimodal inputs to predictive algorithms  have been recently shown to better predict health outcomes compared to single-source approaches across 12 predictive tasks, including 10 distinct chest pathology diagnoses, hospital length-of-stay, and 48 h mortality predictions38. Similarly, the field of sleep medicine may benefit from multi-input clinical data where daytime symptoms, overall clinical history, and multi-sensor recordings could be used to better predict health outcomes and treatment response.Instructions on the timing of blood pressure measurements were not given to participants. Thus, some of the variability in blood pressure may reflect circadian changes across the day39. Thus, it will be important to investigate whether the potential consequences of night-to-night variability in OSA severity are comparable between sexes in a larger cohort with more comparable numbers of women.The decision of the users to purchase a WSA and monitor their blood pressure may represent a selection bias towards those who had preexisting sleep and/or cardiovascular problems. Indeed, users were predominantly male, clearly indicating a sex-specific participation bias. Nonetheless, while no sex differences were detected in the current study in relation to the detected associations with uncontrolled hypertension, the pathophysiology of OSA differs in women versus menDespite these methodological considerations, the identification of strong associations between blood pressure variability, uncontrolled hypertension, and high night-to-night variability in OSA severity provides further support for the unique and important insights that multi-night non-invasive monitoring can yield regarding clinical end-points and potential health risks.Data were acquired retrospectively from 15,526 participants from a consumer-user database of people aged between 18 and 90 years who purchased an under-mattress sleep sensor device WSA and reported blood pressure measurements on at least five separate occasions. Blood pressure measurements were acquired using an FDA-registered Withings blood pressure cuff monitor in the participant’s home, synchronized directly to the Withings database . Data were collected between 1 July 2020 and 30 March 2021. Further inclusion criteria included ≥28 nights of WSA recordings and an average use of ≥4 times per week. All participants provided written consent through the Withings app for their deidentified data to be used for research purposes when signing up for a Withings account and the current study was approved by the Flinders University Human Research Ethics Committee.23. The estimated AHI also has minimal bias with in-laboratory polysomnography-derived AHI when the AHI is considered as a continuous variable22.The WSA is a nearable sleep monitoring device placed under-the-mattress that detects body movements, respiratory rate, heart rate, snoring, and cessation of breathing episodes. These signals are used to estimate the AHI using automated algorithms that show good agreement with in-laboratory polysomnography-derived AHI with high predictive performance to classify mild (89% sensitivity and 75% specificity), moderate-to-severe (88% sensitivity and 88% specificity) and severe OSA (86% sensitivity and 91% specificity)The Withings blood pressure monitor used in conjunction with the WSA comes with an instruction booklet that outlines how to take a blood pressure measurement. The user is instructed to (1) rest for ≥5 min before taking a measurement, (2) be seated in a comfortable position and in a quiet area with legs uncrossed, feet flat on the floor, and back/arm supported (3) not speak during the measurements, and (4) perform the measurement on their left arm. The user has the option of performing a single measurement or taking three consecutive measurements to acquire an average blood pressure value.40 , and OSA variability was defined within each OSA severity category.Hypertension exposure variables were the mean estimated AHI (hereafter referred to as “OSA severity”) and the average standard deviation of estimated AHI (hereafter termed “OSA variability”). OSA severity and variability were calculated for each participant derived from all available nights for each individual. OSA severity categories were defined using standard clinical cut-offs41 across the monitoring period as these thresholds are most commonly applied in previously published papers on the relationship between uncontrolled hypertension and OSA severity35. However, alternate definitions of uncontrolled hypertension were also included in sensitivity analyses. Only participants with five or more independent blood pressure measurements across the assessment period were included in the analysis. Systolic and diastolic blood pressure variability was defined as the standard deviation of the systolic and diastolic blood pressure across all measurements during the recording period.Uncontrolled hypertension was defined as a mean systolic blood pressure ≥140 mmHg or mean diastolic blood pressure ≥90 mmHgAll first-time users of the WSA are prompted to enter their age, sex, height, and weight from which body mass index (BMI) is calculated. These data were either self-reported and manually entered or, in the case of weight, acquired via Withings scales, a device fed directly into the online Withings database.β coefficients) for continuous variables were used to compare the 75th percentile to that of the 25th percentile, using the 25th percentile as the reference.Odds ratio (ORs) and 95% confidence interval (CIs) were determined using logistic-regression models to assess the association between OSA severity, OSA variability, and uncontrolled hypertension. Linear regression was used to assess the association between OSA severity, OSA variability, and blood pressure variability. Arbitrary cut-offs for continuous variables were omitted in favor of restricted cubic spline transformations, which are better suited to non-linearity. Thus, ORs (or 42.OSA severity and variability were first included in separate models (Model 1A and 1B), and then in a combined model (Model 2). All models also included age, BMI, and sex. Likelihood ratios were used to compare predictive performance between models. Collinearity was assessed using variance inflation factors. Interactions between OSA severity and OSA variability and sex were also examined. Although the primary analysis examined OSA severity as a continuous measure, secondary analyses also examined OSA severity categorized according to standard clinical cut-offs and quartiles. Logistic and linear regression was performed in the R programming language, using the rms modeling package43, and was therefore included as a confounder in the third sensitivity analysis. Finally, alternative definitions of hypertension were also investigated, including the home-blood pressure cut-offs of the European/NICE guidelines45 (systolic blood pressure/diastolic blood pressure ≥ 135/85), the AHA guidelines  (≥130/80) and stage 2 hypertension of the NICE guidelines44 (≥150/95).Four sensitivity analyses were conducted to further validate our findings. Firstly, given that blood pressure may vary during the day, we performed a sensitivity analysis where only morning blood pressure, measured between 6 a.m. and 12 p.m., entries were included. Secondly, the analysis was repeated by further adjusting for the number of blood pressure entries to control for potential biases associated with a variable number of blood pressure entries for each participant. Third, total sleep time has been shown to influence AHI certainty46 in R programming language.A potential mediating effect of night-to-night variability in OSA severity in the association between OSA severity and uncontrolled hypertension was investigated using a model depicted in Supplementary Fig. Further information on research design is available in the Supplementary materialREPORTING SUMMARY