The Lancet Commission on precision health: equitable, data-driven health outcomes for all.
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Journal Article↗
Biomedical subjects
Paul W Franks
Publications and source records attributed to Paul W Franks.
Five tenets for advancing evidence-based precision medicine.
Precision medicine for complex diseases uses individual-level characteristics to improve prediction of risk, therapeutic response and prognosis. Many precision medicine studies leverage existing data types and analytic methods to reveal new insights; however, beyond oncology, there has been limited success in translating precision medicine research for complex diseases into clinical practice. Thus, there is a need to identify areas for improvement, particularly in translation-oriented analytical methods and study designs. In this perspective article, we outline five fundamental tenets to enhance the efficient clinical translation of precision medicine research. These tenets focus on addressing (1) heterogeneity in risk, response and prognosis; (2) signal robustness; (3) structured statistical benchmarking against key performance indicators; (4) precision trial designs; and (5) risks and benefits to individuals and society. Our intention is to promote clinically meaningful, reproducible, scalable and equitable health outcomes through precision medicine, beyond those possible through contemporary approaches.
Precision Medicine↗
Increased Genetic Risk for β-Cell Failure Is Associated With β-Cell Function Decline in People With Prediabetes.
Partitioned polygenic scores (pPS) have been developed to capture pathophysiologic processes underlying type 2 diabetes (T2D). We investigated the association of T2D pPS with diabetes-related traits and T2D incidence in the Diabetes Prevention Program. We generated five T2D pPS (β-cell, proinsulin, liver/lipid, obesity, lipodystrophy) in 2,647 participants randomized to intensive lifestyle, metformin, or placebo arms. Associations were tested with general linear models and Cox regression with adjustment for age, sex, and principal components. Sensitivity analyses included adjustment for BMI. Higher β-cell pPS was associated with lower insulinogenic index and corrected insulin response at 1-year follow-up with adjustment for baseline measures (effect per pPS SD -0.04, P = 9.6 × 10-7, and -8.45 μU/mg, P = 5.6 × 10-6, respectively) and with increased diabetes incidence with adjustment for BMI at nominal significance (hazard ratio 1.10 per SD, P = 0.035). The liver/lipid pPS was associated with reduced 1-year baseline-adjusted triglyceride levels (effect per SD -4.37, P = 0.001). There was no significant interaction between T2D pPS and randomized groups. The remaining pPS were associated with baseline measures only. We conclude that despite interventions for diabetes prevention, participants with a high genetic burden of the β-cell cluster pPS had worsening in measures of β-cell function.
Humans↗
Metabolic syndrome and early death: getting to the heart of the problem.
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Humans↗
Gene-lifestyle interaction on risk of type 2 diabetes.
The descriptive epidemiology of type 2 diabetes suggests that gene-lifestyle interactions are critical to the development of the condition. However, unravelling the molecular detail of these interactions is a complex task. The existing literature is based on small intervention studies or cross-sectional observational quantitative trait studies. Our systematic review of the literature identified some evidence of interactions, most notably for a common variant in the PPAR-gamma gene which appears to interact with the nature of dietary fat intake. Other interactions have been reported for adrenoceptors, uncoupling proteins, fatty acid binding proteins, apolipoproteins and lipoprotein lipase. There are, to date, no reports based on the ideal study design which is a case-control study nested within a cohort. To limit the likelihood of false discovery, such studies would need to be large and the search for interaction should be restricted to a priori biologically driven hypotheses. Additional study designs that examine differential response to lifestyle change or test interaction in the context of quantitative trait studies would complement the nested case-control approach, but the emphasis here should be on precision of measurement of both phenotype and lifestyle behaviour.
Aged↗
Physiological evidence for the involvement of peptide YY in the regulation of energy homeostasis in humans.
OBJECTIVE: To explore the potential role of the endogenous peptide YY (PYY) in the long-term regulation of body weight and energy homeostasis. RESEARCH METHODS AND PROCEDURES: Fasting and postprandial plasma PYY concentrations were measured after an overnight fast and 30 to 180 minutes after a standardized meal in 29 (21 men/8 women) non-diabetic subjects, 16 of whom had a follow-up visit 10.8 +/- 1.4 months later. Ratings of hunger and satiety were collected using visual analog scales. Resting metabolic rate (RMR) (15-hour RMR) and respiratory quotient (RQ) were assessed using a respiratory chamber. RESULTS: Fasting PYY concentrations were negatively correlated with various markers of adiposity and negatively associated with 15-hour RMR (r = -0.46, p = 0.01). Postprandial changes in PYY (area under the curve) were positively associated with postprandial changes in ratings of satiety (r = 0.47, p = 0.01). The maximal PYY concentrations achieved after the meal (peak PYY) were negatively associated with 24-hour RQ (r = -0.41, p = 0.03). Prospectively, the peak PYY concentrations were negatively associated with changes in body weight (r = -0.58, p = 0.01). DISCUSSION: Our data indicate that the endogenous PYY may be involved in the long-term regulation of body weight. It seems that this long-term effect was not exclusively driven by the modulation of food intake but also by the control of energy expenditure and lipid metabolism.
Adolescent↗
PGC-1alpha gene and physical activity in type 2 diabetes mellitus.
Exercise stimulates PGC-1alpha gene expression and increases V O2max, the latter of which relates inversely with type 2 diabetes risk. Consistently, low levels of PGC-1alpha mRNA and nucleotide sequence variation at PGC-1alpha associate with lower level of V O2max and increased diabetes risk. Thus, PGC-1alpha sequence variation may interact with physical activity to modify diabetes risk via changes in oxidative energy metabolism.
Base Sequence↗
Gestational glucose tolerance and risk of type 2 diabetes in young Pima Indian offspring.
The in utero environment is a powerful risk factor for type 2 diabetes in offspring, but little is known about the risk conveyed by nondiabetic gestational glucose levels. This issue was explored in 911 nondiabetic Pima Indian mothers and 1,436 of their children. Associations were assessed in multivariate models between maternal third trimester glucose tolerance and indexes of body composition and glycemic control in their children. At parturition, the mothers' ages ranged from 14 to 43 years. Offspring were studied at age 0-39 years. An SD (1.3 mmol/l) of maternal glucose was associated with 56 g higher birth weight (P = 0.0002). This effect persisted when only offspring of normal glucose tolerant mothers were examined (57 g, P < 0.0001). In Cox proportional hazards models, the adjusted hazard rate ratio for offspring risk of diabetes per SD maternal glucose was 1.6 (95% CI 1.3-2.0, P < 0.0001). When only offspring of normal glucose tolerant mothers were examined, the risk was reduced but remained significant (1.3 [1.04-1.71], P = 0.026). In conclusion, maternal glycemia during pregnancy is associated with increased birth weight and risk of diabetes in Pima Indian offspring, even when mothers are normal glucose tolerant during pregnancy. Thus, prevention of offspring type 2 diabetes may require strategies that focus on improving gestational glucose tolerance even within the normal range.
Adolescent↗
Effect of combined movement and heart rate monitor placement on physical activity estimates during treadmill locomotion and free-living.
A placement effect on activity measures from movement sensors has been reported during treadmill and free-living activity. Positioning of electrodes may impact on movement artifact susceptibility as well as surface ECG waveform amplitudes and thus potentially on the precision by which heart rate (HR) is ascertained from such ECG traces. The purpose of this study was to examine the extent to which placement of the combined HR and movement sensor, Actiheart, influences measurement of HR and movement, and estimates of energy expenditure. A total of 24 participants (20-39 years, 45-109 kg, 1.54-2.05 m, 19-29 kg m(-2)) were recruited. Whilst wearing two monitors, one placed at the level of the third intercostal space (upper position) and one just below the apex of the sternum (lower position), study participants performed level walking, incline walking, and level running on treadmill, and completed at least one day of free-living monitoring. Placement differences in HR data quality, movement counts, and energy expenditure (estimated from combined HR and movement) were analyzed with regression techniques. Quality of HR data was generally higher when monitors were placed in the lower position. This effect was more pronounced in men during both treadmill activity (relative risk, RR [95% CI] of noisy HR data in upper vs. lower position, RR=1.3[0.3; 5.6] in women, RR=174[14; 2,156] in men) and during free-living (RR=1.2[0.4; 3.3] in women, RR=25[9.6; 67] in men). There were minor placement differences (< or =8%) in movement counts only in women during incline walking and running. During free-living, no placement effect on counts was observed. In all test scenarios, estimates of energy expenditure from the two positions were not significantly different. Positioning the Actiheart at the level below the sternum may yield cleaner HR data. Regardless of which position is used, this has little or no effect on movement counts and energy expenditure estimates, which is encouraging for studies where research participants may have to position the monitors themselves.
Adult↗
PPARGC1A genotype (Gly482Ser) predicts exceptional endurance capacity in European men.
Animal and human data indicate a role for the peroxisome proliferator-activated receptor-gamma coactivator 1alpha (PPARGC1A) gene product in the development of maximal oxygen uptake (V(O2 max)), a determinant of endurance capacity, diabetes, and early death. We tested the hypothesis that the frequency of the minor Ser482 allele at the PPARGC1A locus is lower in World-class Spanish male endurance athletes (cases) [n = 104; mean (SD) age: 26.8 (3.8) yr] than in unfit United Kingdom (UK) Caucasian male controls [n = 100; mean (SD) age: 49.3 (8.1) yr]. In cases and controls, the Gly482Ser genotype met Hardy-Weinberg expectations (P > 0.05 in both groups tested separately). Cases had significantly higher V(O2 max) [73.4 (5.7) vs. 29.4 ml x kg(-1) x min(-1) (3.8); P < 0.0001] and were leaner [body mass index: 20.6 (1.5) vs. 27.6 kg/m2 (3.9); P < 0.0001] than controls. In unadjusted chi2 analyses, the frequency of the minor Ser482 allele was significantly lower in cases than in controls (29.1 vs. 40.0%; P = 0.01). To assess the possibility that genetic stratification could confound these observations, we also compared Gly482Ser genotype frequencies in Spanish (n = 164) and UK Caucasian men (n = 381) who were unselected for their level of fitness. In these analyses, Ser482 allele frequencies were very similar (36.9% in Spanish vs. 37.5% in UK Caucasians, P = 0.83), suggesting that confounding by genetic stratification is unlikely to explain the association between Gly482Ser genotype and endurance capacity. In summary, our data indicate a role for the Gly482Ser genotype in determining aerobic fitness. This finding has relevance from the perspective of physical performance, but it may also be informative for the targeted prevention of diseases associated with low fitness such as Type 2 diabetes.
Adult↗
Leptin predicts a worsening of the features of the metabolic syndrome independently of obesity.
OBJECTIVE: The term metabolic syndrome (MS) describes a cluster of cardiovascular risk factors including dyslipidemia, glucose intolerance, insulin resistance, and hypertension. Obesity increases the risk of MS, but as obesity is neither necessary nor sufficient to cause the syndrome, there is considerable interest in identifying obesity-independent pathways. One such pathway may involve the actions of the adipokine leptin, which is associated cross-sectionally with MS and prospectively with coronary heart disease and stroke, independently of obesity. Our goal was to test the hypothesis that leptin predicts the development of the features of MS independently of obesity. RESEARCH METHODS AND PROCEDURES: This study used a prospective population-based cohort of 748 middle-aged whites in whom baseline measures of leptin and repeated measurement of the subcomponents of the MS at 5 and 10 years were available. The features of the MS were characterized as five factors (obesity, dyslipidemia, elevated blood pressure, glucose intolerance, and insulin resistance), which were combined to create an MS summary score. RESULTS: Baseline leptin significantly predicted the development of obesity (p = 0.001) and, after adjustment for BMI, development of glucose intolerance (p = 0.016) and insulin resistance (p < 0.0001). Leptin levels did not independently predict a change in lipids or blood pressure. Leptin levels significantly predicted the development of the MS (p = 0.036), independently of baseline BMI. DISCUSSION: Leptin predicts the development of the MS independently of baseline obesity. This association is specifically related to the development of glucose intolerance and insulin resistance. The extent to which these relationships are explained through residual confounding by obesity remains to be determined.
Adult↗
Physical activity energy expenditure predicts changes in body composition in middle-aged healthy whites: effect modification by age.
BACKGROUND: It is unclear whether physical activity energy expenditure (PAEE) predicts changes in body composition. OBJECTIVE: The objective was to describe the independent associations between PAEE and changes in body composition in a population-based cohort. DESIGN: This was a prospective population-based study conducted in 739 (311 men and 428 women) healthy middle-aged (median age: 53.8 y) whites. The median follow-up was 5.6 y. PAEE (MJ/d) was assessed by heart rate monitoring, individually calibrated by using the FLEX heart rate method. Fat mass (FM) and fat-free mass (FFM) were assessed by bioimpedance. RESULTS: Body weight (BW) at follow-up was significantly related to baseline PAEE (P < 0.05) after adjustment for sex, baseline age, FM, FFM, and follow-up time. A significant interaction between PAEE and age (P = 0.023) was observed. After the subjects were stratified (above and below the median for age), BW increased by a mean (+/-SD) of 1.7 +/- 5.9 kg (P < 0.0001) in the younger cohort. In this group, follow-up FM was significantly associated with baseline PAEE (P = 0.036) after adjustment for confounders. In the older cohort, BW did not change between baseline and follow-up. In this group, in contrast with the younger population, follow-up BW, FM, and FFM were all significantly and positively associated with baseline PAEE (P < 0.01 for all). CONCLUSIONS: Baseline PAEE predicts a change in FM in younger adults, who as a group gained weight in this study. In contrast, baseline PAEE in older adults--who were on average weight stable--is associated with a gain in BW, which was explained by an increase in FM and FFM.
Adipose Tissue↗
Habitual physical activity in children: the role of genes and the environment.
BACKGROUND: Understanding the factors that contribute to physical inactivity in children is important because sedentary behavior strongly relates to metabolic disorders such as obesity and diabetes. OBJECTIVE: We aimed to quantify the genetic and environmental influences on physical activity energy expenditure (PAEE) in 100 sex-concordant dizygotic (n = 38) and monozygotic (n = 62) twin pairs aged 4-10 y. DESIGN: Resting metabolic rate (RMR) was assessed by using respiratory gas exchange, total energy expenditure (TEE) by using doubly labeled water, and body composition by using dual-energy X-ray absorptiometry. Structural equation modeling was used to partition the phenotypic variance into additive genetic (a2) and common (c2) and unshared (e2) environmental components. RESULTS: Because PAEE [TEE - (RMR + 0.1 x TEE)] depends on body weight, which is highly heritable, we tested several models: 1) after adjustment for age, sex, ethnicity, study date, season, and weight, a2 explained none of the phenotypic variance in PAEE (95% CI: 0%, 38%), whereas c2 and e2 accounted for 69% (33%, 77%; P = 0.001) and 31% (23%, 39%; P < 0.001) of the variance, respectively; 2) after adjustment for the cofactors in model 1, a2 explained 19% of the phenotypic variance in TEE (0%, 60%; P = 0.13), whereas c2 and e2 accounted for 59% (16%, 79%; P = 0.007) and 23% (17%, 31%; P < 0.0001) of the variance, respectively; 3) in models adjusted as above (excluding weight), a2 explained no variance in physical activity level (TEE/RMR) (0%, 32%; P = 0.50), whereas c2 and e2 explained 65% (34%, 60%; P = 0.001) and 35% (28%, 45%; P < 0.0001) of the variance, respectively. CONCLUSIONS: Our data suggest that the familial resemblance in physical activity in these children is explained predominantly by shared environmental factors and not by genetic variability.
Absorptiometry, Photon↗
Comparing the roles of physical activity and fitness in arterial stiffness: how important is exposure measurement error?
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Arteries↗
Variation in the eNOS gene modifies the association between total energy expenditure and glucose intolerance.
Endothelium-derived nitric oxide (NO) facilitates skeletal muscle glucose uptake. Energy expenditure induces the endothelial NO synthase (eNOS) gene, providing a mechanism for insulin-independent glucose disposal. The object was to test 1) the association of genetic variation in eNOS, as assessed by haplotype-tagging single nucleotide polymorphisms (htSNPs) with type 2 diabetes, and 2) the interaction between eNOS haplotypes and total energy expenditure on glucose intolerance. Using multivariate models, we tested associations between eNOS htSNPs and diabetes (n = 461 and 474 case and control subjects, respectively) and glucose intolerance (two cohorts of n = 706 and 738 U.K. and Spanish Caucasians, respectively), and we tested eNOS x total energy expenditure interactions on glucose intolerance. An overall association between eNOS haplotype and diabetes was observed (P = 0.004). Relative to the most common haplotype (111), two haplotypes (121 and 212) tended to increase diabetes risk (OR 1.22, 95% CI 0.96-1.55), and one (122) was associated with decreased risk (0.58, 0.39-0.86). In the cohort studies, no association was observed between haplotypes and 2-h glucose (P > 0.10). However, we observed a significant total energy expenditure-haplotype interaction (P = 0.007). Genetic variation at the eNOS locus is associated with diabetes, which may be attributable to an enhanced effect of total energy expenditure on glucose disposal in individuals with specific eNOS haplotypes. Gene-environment interactions such as this may help explain why replication of genetic association frequently fails.
Aged↗
Physical activity energy expenditure predicts progression toward the metabolic syndrome independently of aerobic fitness in middle-aged healthy Caucasians: the Medical Research Council Ely Study.
OBJECTIVE: To examine over a period of 5.6 years the prospective associations between physical activity energy expenditure (PAEE), aerobic fitness (Vo(2max)), obesity, and the progression toward the metabolic syndrome in a population-based cohort of middle-aged men and women (n = 605) who were free of the metabolic syndrome at baseline. RESEARCH DESIGN AND METHODS: PAEE was measured objectively by individually calibrated heart rate against energy expenditure. Vo(2max) was predicted from a submaximal exercise stress test. Fat mass and fat-free mass were assessed by bio-impedance. A metabolic syndrome score was computed by summing the standardized values for obesity, hypertension, hyperglycemia, insulin resistance, hypertriglyceridemia, and the inverse level of HDL cholesterol and expressed as a continuously distributed outcome. Generalized linear models were used to examine the independent prospective associations between PAEE and Vo(2max) and the metabolic syndrome score after adjusting for sex, baseline age, smoking, socioeconomic status, follow-up time, and baseline phenotypes. RESULTS: PAEE predicted progression toward the metabolic syndrome, independent of baseline metabolic syndrome, body fat, Vo(2max), and other confounding factors (standardized beta = -0.00085, P = 0.046). This association was stronger when excluding the adiposity component from the metabolic syndrome (standardized beta = -0.0011, P = 0.035). Vo(2max) was not an independent predictor of the metabolic syndrome after adjusting for physical activity (standardized beta = 0.00011, P = 0.93). CONCLUSIONS: PAEE predicts progression toward the metabolic syndrome independent of aerobic fitness, obesity, and other confounding factors. This finding underscores the importance of physical activity for metabolic disease prevention even when an improvement in aerobic fitness is absent.
Adult↗
White-coat hypertension and risk of stroke: do the data really tell us what we need to know?
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Humans↗
Interaction between an 11betaHSD1 gene variant and birth era modifies the risk of hypertension in Pima Indians.
11beta-hydroxysteroid dehydrogenase type 1 (11betaHSD1) is a candidate gene for hypertension, diabetes, and obesity through altered glucocorticoid production. This study explored the association of 11betaHSD1 gene variants with diabetes, hypertension, and obesity in a longitudinal population study of American Indians (N=918; exams=5508). In multivariate mixed models assuming an additive effect of genotype, a 5' upstream variant (rs846910) was associated with blood pressure (diastolic blood pressure beta=1.58 mm Hg per copy of the A allele, P=0.0008; systolic blood pressure beta=2.28 mm Hg per copy of the A allele, P=0.004; mean arterial blood pressure beta=1.83 mm Hg per copy of the A allele, P=0.0006) and hypertension (odds ratio=1.27 per copy of the A allele, P=0.02). However, birth date modified these associations (test for interaction: diastolic blood pressure P=0.16; systolic blood pressure P=0.007; mean arterial blood pressure P=0.01), such that the magnitude and direction of association between genotype and blood pressure changed with time. Finally, in models controlling for potential confounding by population stratification, we observed evidence of within-family effects for blood pressure (diastolic blood pressure beta=1.77 mm Hg per copy of the A allele, P=0.004; systolic blood pressure beta=2.04 mm Hg per copy of the A allele, P=0.07; mean arterial blood pressure beta=1.85 mm Hg per copy of the A allele, P=0.01) and for hypertension (odds ratio=1.26 per copy of the A allele; P=0.08). No association was observed for obesity. Associations with diabetes were similar in magnitude as reported previously but were not statistically significant. These data demonstrate association between genetic variability at 11betaHSD1 with hypertension, but these effects are modified by environmental factors.
11-beta-Hydroxysteroid Dehydrogenase Type 1↗