Despite this difference, this small proportion of older Japanese in the stable overweight group had the lowest risk of death, which is similar to the observations in the Western populations described above. These consistent findings i. There is considerable recent interest in the literature for longitudinal analyses of visit-to-visit variability VVV of blood pressure BP in relation to CVD and mortality in both clinical settings and in general populations. Diaz et al. Although limited by a small number of studies available for the meta-analysis, the review confirmed that modest associations between VVV of BP and CVD and all-cause mortality have been found in the published research.
Most studies investigated VVV of BP in select populations such as secondary analyses of randomized controlled trials or patients with or at high risk for vascular diseases in clinical settings. The earliest of those dates back to Hofman et al. Muntner et al. The variability of BP defined as SD and CV across visits in this study was assessed from three consecutive blood pressure readings during three separate visits from to Suchy-Dicey et al.
Then intra-individual variability the primary variable of interest was calculated as the square root of the variance from the residuals from the participant-specific regressions. They found that intra-individual SBP variability in older adults was significantly associated with increased risk of total mortality and of incident MI but was not associated with the risk of stroke which may be caused by specifics of the variability measure and the study design.
The authors also discussed potential biological mechanisms by which the long-term variability in BP may affect risks of mortality or CVD. For example, the chronic large fluctuations of BP may accelerate wear and tear of the vascular tissue, and thus contribute to the development or severity of atherosclerosis. Oppositely, the chronic fluctuations of BP may reflect the underlying pathological process manifested in vessel sclerosis and increased stiffness Karwowski et al.
The existence of many hypotheses on the mechanisms by which SBP variability may increase all-cause mortality indicates that more research is needed to elucidate exact etiologic pathways of the observed associations before proposing clinical implications for individual patients. In addition, it is important to conduct more studies with non-Western populations to find out how or whether these observations can be generalized.
For example, Yinon et al. This inconsistency with Western studies can be due to substantial differences between participants of this study and Western studies in the levels of biomarkers e. Poortvliet et al. The Leiden plus Study is a prospective population-based study among year-old inhabitants of the city of Leiden the Netherlands which collects biomarker data and follow-up information thus providing an opportunity to explore dynamics of biomarkers in relation to mortality in the older ages.
They found that at the oldest old ages, both decreasing trend in SBP over the previous five years and the current SBP value independently contribute to prediction of all-cause mortality. One interesting and important observation from this study is that a decreasing trend in SBP in the preceding five years in participants with a low SBP at age 90 predicted a more than doubled mortality risk compared to participants with an average five-year trend in SBP and a high SBP at age This suggests that keeping SPB relatively high and stable in advanced years of life may be more important for survival toward extreme ages than reducing risks of particular health conditions such as, e.
Indeed, excessive reduction in SBP at older ages might promote physical frailty, a major risk factor for all-cause mortality in the very old. We additionally explored the trade-off-like influence of risk factors for major diseases and senescence related causes, such as physical frailty, on all-cause mortality in the elderly in a recent review paper Ukraintseva et al. These traditional metabolic risk factors and indicators of health and disease were measured annually during a five-year follow-up period giving up to six measurements per participant.
They found that mortality was associated with stronger declines in BMI, total cholesterol TC levels, and blood pressures and with weaker increases in HDL cholesterol levels. Similar effects were detected in Yashin et al. The second major finding in van Vliet et al. The distinct profile identified in such analyses annual changes in total and LDL cholesterol, albumin, and hemoglobin levels showed the strongest correlation with respective principal component is suggestive of underlying wasting disease.
A distinct example of epidemiological studies useful for analyses of trajectories of biomarkers and mortality is the FHS Dawber et al. Below we briefly summarize recent results on available empirical evidence on such relationships evaluated from the FHS data.
Several earlier studies from the —s used the FHS data to explore associations between changes in selected biomarkers weight, BMI, lipid levels, BP and morbidity and mortality risks, considering impact of other factors such as smoking cessation or diet. For example, Higgins et al. The authors found that relative risks of death from CVD and all causes combined were significantly greater in participants whose weight or BMI decreased after adjusting for age and other risk factors Higgins et al.
At the same time, weight loss was associated with improvements in blood pressure and cholesterol levels. Indeed, at the oldest old ages risks of many complex diseases e. This indicates that postponed manifestation of physical senescence may be more important for achieving extreme longevity than simply having a lower disease risk. The apparent trade-off between the detrimental impact of weight loss on mortality rate and its beneficial impact on some risk factors for disease was further explored by Allison et al.
This indicates that most of the detrimental effect of the weight loss on mortality could come from the muscle loss, which typically accompanies senescence. This is in line with the above consideration that postponed manifestations of physical senescence such as physical frailty, sarcopenia, muscle atrophy, etc. Hofman et al.
They found, in particular, that this dynamic variable slope was associated with mortality from all causes so that individuals with larger slopes i. Kreger et al. The TC variability was computed as the square root of the mean squared error for observations fitted by a linear function. The results showed a positive association of such TC variability with all-cause mortality in men and cardiovascular and coronary diseases incidence and mortality in both sexes with risk ratios for highest vs.
These earlier studies using the FHS data were based on shorter follow-up periods than this ongoing study can provide nowadays. The additional follow up period in the FHS allows for investigating the dynamics of biomarkers at wider age ranges including older ages where such individual trajectories may have a more complex shape e.
Several papers considered dynamics of biomarkers in the FHS in relation to mortality and morbidity outcomes using the more extended recent FHS data. Yashin et al. The analysis demonstrated that indeed the survival patterns in individuals who survived to age of 65 depended on the behavior of the physiological indices between ages 40 and 60 years. This indicates that the deviant dynamics of biomarkers with age may result in higher mortality risks.
For biomarkers with almost monotonic age trajectories e. For biomarkers with non-monotonic trajectories inverse U-shape, such as BMI, TC, SBP, DBP , the dynamic characteristics were: the rate of increase, age at reaching the maximal value, the rate of decline after reaching the maximum, and the variability at biomarker- and sex-specific age intervals. The analyses showed that these dynamic characteristics of trajectories of biomarkers at middle and old ages indeed influence subsequent mortality risk at older ages.
Availability of genetic information in the FHS allows computations of average age trajectories of biomarkers in carriers of different genotypes or alleles. Arbeev et al. This observation is in line with findings in Yashin et al.
These results indicate that harmful effects from the sustained abnormal levels of these variables e. Several studies reported significant longitudinal changes in CSF biomarkers in AD patients, while others did not; some suggested that the shape of the association of AD biomarkers with disease severity can sometimes be nonlinear e.
Other groups utilized longitudinal imaging measures to estimate temporal trajectories of cortical amyloid deposition before the onset of clinical symptoms to use them as potential predictors of progression to AD Bilgel et al.
Predicting coronary artery disease risk based on multiple longitudinal biomarkers started to be explored as well Yang et al. There have been found associations between habitual physical activity PA and changes in PA over time and onset of CHD in 3, ambulatory men during a median of 11 years of follow-up, favoring even light PA Jefferis et al. All these studies highlight the importance of considering the dynamics of biomarkers in relation to mortality, health outcomes, and the processes of aging.
Joint influence of different aging-related mechanisms can manifest itself in the observed dynamics of biomarkers and its relation to mortality risk. But first we proceed in the next section with a brief overview of several methods to construct cumulative measures based on multiple biomarkers. Such composite indices are appealing as they often are better predictors of mortality than single biomarkers. They may also have rationale from both theoretical biological as well as practical statistical perspectives.
Cumulative Measures Based on Multiple Biomarkers and Mortality Risk The process of aging results in biological changes in different organs and physiological systems in an organism. Such changes can accumulate over time in a complex and non-additive fashion across different regulatory systems and their cumulative effect manifests in increasing physiological dysregulation, development of chronic diseases and death.
Summary measures based on combinations of biomarkers from different physiological systems aim at capturing this complex effect of aging on different systems and they are expected to be better predictors of mortality and health-related outcomes than individual biomarkers. One of such measures which found broad applications to different data is allostatic load AL.
Although there are still some refinements and discussions on the concepts of allostasis and AL McEwen and Wingfield, ; Romero et al. The biomarkers included in the original definition of AL were: SBP and DBP represent cardiovascular activity , waist-hip ratio reflect more long-term levels of metabolism and adipose tissue deposition , serum HDL and total cholesterol levels indicating long-term atherosclerotic risk ; blood plasma levels of total glycosylated hemoglobin an integrated measure of glucose metabolism during a period of several days ; serum dehydroepiandrosterone sulfate a functional HPA axis antagonist ; hour urinary cortisol excretion an integrated measure of hour HPA axis activity ; hour urinary norepinephrine and epinephrine excretion levels integrated indices of hour sympathetic nervous system activity Seeman et al.
Thus, the components of this score reflect parameters of different regulatory systems contributing to wear and tear on the body. Various modifications of the original operationalization of AL and other approaches to its computation have been suggested in the literature see detailed discussion in recent review papers by Beckie, ; Juster et al.
The association of AL and mortality has been documented in different studies. For example, Seeman et al. These findings supported the concept that AL is a measure of cumulative biological burden that can provide insight into the cumulative risks to health from biological dysregulation across multiple regulatory systems Seeman et al.
With a few exceptions, such studies evaluated associations between baseline measurement of AL and subsequent mortality. Several studies explored the association between dynamic changes in AL and mortality. Karlamangla et al. They observed that individuals with increased AL score over a 2. The fact that both baseline AL and the change in AL were independently and significantly associated with subsequent all-cause mortality suggests that not only the current state of respective biomarkers but also the history of abnormal values of these biomarkers contribute to mortality risk.
Hwang et al. They also found that both static and dynamic measures of AL were related to mortality and a fast increase in AL was associated with significantly higher mortality risk compared to participants with declining AL. There are also other recent studies exploring the dynamics of AL in longitudinal studies in relation to other outcomes see, e.
However, in general, the potential of longitudinal studies in this area is largely underexplored and it is recognized in the literature that investigation of AL measured at multiple time points in population-representative longitudinal studies is one of the priorities for future research Beckie, Another popular approach to construct cumulative scores is a frailty index FI Mitnitski et al.
This score was developed to quantify frailty based on accumulation of various health-related deficits. An FI is computed as the number of deficits in an individual divided by the total number of available deficits so that the theoretical range of FI is between 0 and 1 although in practice the maximal values of FI in different studies are close to 0.
The FI or DI have been investigated by several research groups in applications to different datasets from different countries and they showed remarkable robustness of results to data collection methods clinical vs. The FI is a strong predictor of adverse outcomes including death and it outperforms chronological age as a predictor of mortality see discussion summarizing these topics in Mitnitski and Rockwood, Section 3.
Although various types of deficits can be combined to produce an FI such as health conditions, symptoms, diseases, etc. Howlett et al. This observation was later confirmed by Rockwood et al. Mitnitski et al. These studies indicated that different biomarkers combined into the FI can be used to predict mortality and the observation that so many biomarkers contribute to mortality prediction reflects the systemic characteristics of aging and mortality which such cumulative measures as the FI can help reveal and which may be masked when individual biomarkers are analyzed Howlett et al.
Both indices also contributed separately in the Cox regression analyses although there was only a moderate increase in AUC for the combined index compared to the separate ones 0. The highest levels of the two indices were similar 95th percentile 0. As the authors indicate, combining the biomarker-based indices with those based on clinically detectable deficits can help better identify individuals at higher risk of death. It is also important to validate how such biomarker-based indices perform in other studies, their sensitivity to the choice of biomarkers and thresholds used for dichotomization in the construction of biomarker-based indices, as well as to evaluate to what extent such indices can improve prediction of adverse outcomes other than mortality.
Several methods of BA computations were developed but there is a lack of consensus on what is the most optimal one. Recently, Levine compared predictive ability of several BA algorithms and found that the Klemera and Doubal method was the most reliable predictor of mortality performing significantly better than chronological age. The fundamental relationship between the FI and BA was discussed in recent publications by Mitnitski and Rockwood Mitnitski and Rockwood, , but there is some disagreement on this Levine, Note that BA, computed from cross-sectional data, cannot provide an input on longitudinal changes within a person.
In a recent publication, Belsky et al. The authors found that such longitudinal measure allows quantification of the pace of coordinated physiological deterioration across multiple organ systems e. These findings highlight the importance of incorporating multiple longitudinal repeated measures of biomarkers tracking changes across different organ systems in studies of aging.
It is also important to investigate how this or different dynamic measures can be applied to studies with wider ranges of ages including the oldest old ages where the dynamics of different biomarkers follows non-linear e. Another approach to construct a composite measure from multiple biomarkers has been presented in Cohen et al. They suggested a measure of physiological dysregulation based on statistical distance specifically, Mahalanobis distance, De Maesschalck et al.
The biomarkers used for construction of DM in Cohen et al. These blood markers have been separately or as panels used in various studies of health. However, they are not typically used in aging studies all together, as a combined set of blood biomarkers. The authors showed that such DM correlated positively with age, increased over time in individuals and higher values of DM predicted higher subsequent mortality.
The results supported hypotheses of simultaneous dysregulation in multiple systems and showed that DM provides an approach to reduce high-dimensional biomarker space into a single measure which summarizes information about physiological dysregulation in an aging organism.
Several publications about DM appeared in the literature since the original paper by Cohen et al. Milot et al. The results for mortality were replicated in three longitudinal studies thus confirming the role of dynamics of physiological dysregulation as represented by DM as the predictor of mortality in different studies. Cohen et al. They observed that, if a substantial number of biomarkers are included, the specific choice of biomarkers has little effect on the resulting measure.
This provides a parallel with another measure, FI, which is constructed differently counting the number of deficits but it also shows minimal sensitivity to the choice of deficits. Thus investigation of relationships between both measures seems interesting but it is yet to be done Cohen et al.
There is variety of methods appropriate for such analyses see, e. In many cases, this may be sufficient to generate useful and interpretable results. However, in some areas these same models can be viewed as rather too simplistic, especially if they do not take into account different theoretical concepts accumulated in the field.
The SPM methodology provides an example where both statisticians and biologists may find some useful characteristics from their perspectives. Then, which stochastic process can or should be used to represent this stochasticity in the model of aging? Specifically, in the long run, such a process tends to drift towards its equilibrium state long-term mean which has a natural biological interpretation in terms of homeostatic regulation.
Dnipropetrovsk became an important cultural and educational centre with ten colleges and a State University. Peasants had died en masse during the Holodomor of — The percentage of residents recorded as Ukrainian rose from 36 percent of the population in to The monumental inscription in Russian does not mention Jews by name, but speaks of "20, civilians.
The Holocaust in Dnipropetrovsk reduced the city's remaining Jewish population, estimates for which range from 55, to 30,, to just As early as July , the State Committee of Defence in Moscow decided to build a large military machine-building factory in Dnipropetrovsk on the location of the pre-war aircraft plant. In December , thousands of German prisoners of war began construction and built the first sections and shops in the new factory. This was the foundation of the Dnipropetrovsk Automobile Factory.
In the administration of this automobile factory opened a secret design office, designated OKB , to construct military missiles and rocket engines. In , the secret Plant No. Yuzhmash became a significant factor in the arms race of the Cold War Nikita Khrushchev boasted in that it was producing rockets "like sausages". Its citizens were held by Communist authorities to a higher standard of ideological purity than the rest of the population, and their freedom of movement was severely restricted.
It was not until , during perestroika , that Dnipropetrovsk was opened to international visitors and civil restrictions were lifted. In In Kyiv In Odesa these numbers were 8. These fed into underground student circles where they promoted interest in the " Ukrainian Sixtiers ", in Ukrainian history , especially of Ukrainian Cossacks , and in the revival of the Ukrainian language. Occasionally the blue and yellow flag of independent Ukraine was unfurled in protest. Thousands of high-school and college students had become ham radio enthusiasts, recording and rebroadcasting western popular music.
Annual KGB reports regularly drew a connection between enthusiasm for western pop culture and anti-Soviet behavior. Some of the activists involved in this "disco movement" went on in the s to engage in their own illicit tourist and music enterprises, and several later became influential figures in Ukrainian national politics, among them Yulia Tymoshenko , Victor Pinchuk , Serhiy Tihipko , Ihor Kolomoyskyi and Oleksandr Turchynov.
They spearheaded the internal party coup that in saw Brezhnev replace Nikita Khrushchev as General Secretary of the Communist Party of the Soviet Union and call a halt to further reform. In June and July , Dnipropetrovsk experienced a wave of random video-recorded serial killings that were dubbed by the media as the work of the " Dnipropetrovsk maniacs ".
Opposition politicians claimed to see the hand of President Viktor Yanukovych intent on disrupting the October Ukrainian parliamentary election and installing a presidential regime. On 26 January , 3, anti- Ukrainian President Viktor Yanukovych and pro- Euromaidan activists attempted but failed to capture the Regional State Administration building.
Dnipropetrovsk remained relatively quiet during the pro-Russian unrest in Ukraine , with pro-Russian Federation protestors outnumbered by those opposing outside intervention. To comply with the decommunization law the city was renamed Dnipro in May , after the river that flows through the city.
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This position will conduct initial We are looking for a full-time Compliance Competitive pay benefits and training program. Hungry people, not criminals, built the Moscow-Volga Canal. Collectivization caused famine throughout Russia. Kulaks Evgenij Filibog received fifteen years for espionage. After Uncle Frol obtained a threshing machine, he gave his grain to the state. Communists believed prisoners were enemies of the state.
A "death cart" gathered corpses at night and delivered them to mass graves. Great Achievement Konstantin Kravchenko received ten years for economic sabotage. After a dam burst, the "Frog Foreman" pushed prisoners into a hole to block the water from washing away foundations. Opening Ceremony Guards informed Filimonova that prisoners were buried in the concrete. Stalin appeared on his boat. The head of construction was arrested immediately after giving a speech. Petersburg, Russia Peter the Great built the city using slaves.
Those who committed atrocities do not want their actions remembered. Revisiting the Past Andrei Tchernavin's family escaped the Soviet Union after his father was arrested. Citizens heard sirens when raids occurred. Possessing gold was a death sentence. Stalin's government conducted executions and unjustified arrests, but provided education, health care, and housing.
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