The study was supported by Novartis.

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Table 22.2 Pharmacological Agents Available as Generics for Controlling Hypertension and Reducing Cardiovascular Risk in Many Countries Class Common examples (alphabetic) Angiotensin-converting enzyme inhibitors Captopril, enalapril, lisinopril, perindopril, ramipril Angiotensin receptor blockers Candesartan, losartan, olmesartan, telmisartan, valsartan Calcium channel blockers Amlodipine, cilnidipine, lercanidipine, nifedipine Diuretics (thiazides and thiazide-like) Bendroflumethiazide, chlorthalidone, chlorothiazide, hydrochlorothiazide, indapamide Beta blockers Atenolol, bisoprolol, carvedilol, metoprolol, nebivolol, propranolol Mineralocorticoid receptor antagonists Eplerenone, spironolactone Alpha blockers Doxazosin, prazosin Others Clonidine, hydralazine, methyldopa, minoxidil, reserpine Note: Preferred antihypertensive drugs in women of reproductive age with intention for conception and for pregnant and breastfeeding women are methyldopa, nifedipine, and hydralazine.

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Patients with asthma have many barriers in the way of optimal treatment. These include a failure to recognize warning symptoms, belief in a permanent cure; not continuing treatment for as long as needed; and, an inclination to seek complimentary medicines.

High amounts of sitting time are associated with increased risks of cardiovascular disease (CVD) and mortality in high-income countries, but it is unknown whether risks also increase in low-and middle-income countries.OBJECTIVE To investigate the association of sitting time with mortality and major CVD in countries at different economic levels using data from the Prospective Urban Rural Epidemiology study. DESIGN, SETTING, AND PARTICIPANTSThis population-based cohort study included participants aged 35 to 70 years recruited from January 1, 2003, and followed up until August 31, 2021, in 21 high-income, middle-income, and low-income countries with a median follow-up of 11.1 years.EXPOSURES Daily sitting time measured using the International Physical Activity Questionnaire. MAIN OUTCOMES AND MEASURESThe composite of all-cause mortality and major CVD (defined as cardiovascular death, myocardial infarction, stroke, or heart failure). RESULTSOf 105 677 participants, 61 925 (58.6%) were women, and the mean (SD) age was 50.4 (9.6) years. During a median follow-up of 11.1 (IQR, 8.6-12.2) years, 6233 deaths and 5696 major cardiovascular events (2349 myocardial infarctions, 2966 strokes, 671 heart failure, and 1792 cardiovascular deaths) were documented. Compared with the reference group (<4 hours per day of sitting), higher sitting time (Ն8 hours per day) was associated with an increased risk of the composite outcome (hazard ratio [HR], 1.19; 95% CI, 1.11-1.28; P for trend < .001), all-cause mortality (HR, 1.20; 95% CI, 1.10-1.31; P for trend < .001), and major CVD (HR, 1.21; 95% CI, 1.10-1.34; P for trend < .001). When stratified by country income levels, the association of sitting time with the composite outcome was stronger in low-income and lower-middle-income countries (Ն8 hours per day: HR, 1.29; 95% CI, 1.16-1.44) compared with high-income and upper-middle-income countries (HR, 1.08; 95% CI, 0.98-1.19; P for interaction = .02). Compared with those who reported sitting time less than 4 hours per day and high physical activity level, participants who sat for 8 or more hours per day experienced a 17% to 50% higher associated risk of the composite outcome across physical activity levels; and the risk was attenuated along with increased physical activity levels.CONCLUSIONS AND RELEVANCE High amounts of sitting time were associated with increased risk of all-cause mortality and CVD in economically diverse settings, especially in low-income and lower-middle-income countries. Reducing sedentary time along with increasing physical activity might be an important strategy for easing the global burden of premature deaths and CVD.

The increasing burden of obesity worldwide and its effect on cardiovascular disease (CVD) risk is an opportunity for evaluation of preventive approaches. Both obesity and CVD have a genetic background and polymorphisms within genes which enhance expression of variant proteins that influence CVD in obesity. Genome‐based prediction may therefore be a feasible strategy, but the identification of genetically driven risk factors for CVD manifesting as clinically recognized phenotypes is a major challenge. Clusters of such risk factors include hyperglycaemia, hypertension, ectopic liver fat, and inflammation. All involve multiple genetic pathways having complex interactions with variable environmental influences. The factors that make significant contributions to CVD risk include altered carbohydrate homeostasis, ectopic deposition of fat in muscle and liver, and inflammation, with contributions from the gut microbiome. A futuristic model depends on harnessing the predictive power of plausible genetic variants, phenotype reversibility, and effective therapeutic choices based on genotype–phenotype interactions. Inverting disease phenotypes into ideal cardiovascular health metrics could improve genetic and epigenetic assessment, and form the basis of a future model for risk detection and early intervention.