1. Introduction
Since the beginning of time [1], the universe has been changing. Heraclitus, an ancient Greek philosopher (535-475 BC), strongly emphasized ‘change’ in the universe (universal flux) [2]. He asserted that “Life is Flux” [3], which means that the only constant in life is “change”. To survive in a changing environment, living systems must adapt their internal systems to external cues [4]. This adaptation is observed in all living systems and organized at the cellular level by the molecular clocks; however, the adaptive ability is decreased during aging [4,5,6,7]. Human metabolism is a dynamic process that shows numerous rhythmic or non-rhythmic variations and consequently, variations are inseparable parts of the human metabolism. It should be noted that metabolic variations are not only necessary for the adaptations but they also are required for internal organizations and disease prevention. Moreover, the changes in the amounts of these metabolic variations could be applied for the prediction of disease or the introduction of individual therapeutic methods based on their specific symptoms [8,9,10,11,12,13]. On the other hand, rhythmic or non-rhythmic variations could be applied to 3P (prevention, prediction, and personalized) medicine.
The variations in the concentrations/activities of molecules observed in individuals can be classified as systematic and random variations. Systematic variations are partly predictable, i.e., in healthy individuals, the time, level, and duration of systematic variations grossly can be predicted before the variation occurs. On the other hand, the time and duration of random biological variations (BV) cannot be predicted as the systematic variations but can be measured using appropriate measurement and statistical procedures. To avoid terminological confusion, in this manuscript, rhythms (or physiological/biological rhythms) and biological variations (BV) are used to denote systematic and random variations, respectively.
In biological systems, rhythmic variations in the concentrations/activities of molecules can be observed over a wide range of time scales from fractions of a second to years. These variations are classified into three subgroups: ultradian [14], circadian [11], and infradian [15]. On the other hand, random BV can be observed using serial measurements of analytes from samples taken at the same time on different days. For each analyte, two types of random BVs have been determined: between-subject BV (CVG), which is the variation among the set points of the analyte from different individuals, and within-subject BV (CVI), which is the variation of the analyte around its homeostatic set point.
This systematic review aims to describe the two main types of variations: biological rhythms and random biological variation. Accordingly, we have summarized some of the research describing the practical use of these variations in personalized clinical and laboratory medicine in the following sections.
PubMed and Web of Science databases were chosen as the main databases. Both databases were searched for biological variation and physiological rhythm articles in English language without time restrictions with the terms “Biological variation, Within-subject biological variation, Between-subject biological variation, Physiological rhythms, Ultradian rhythms, Circadian rhythm, Infradian rhythms”. The study was carried out between November 2021 and May 2023. Totally, 225 publications including 217 papers, 5 books, 2 websites, and 1 guideline were selected and included in the study. A total of 152 (70.0%) papers were published within the last decade.
2. Biological Rhythms
Biological rhythms are the inherent rhythmicity observed in living systems that are characterized by any behavioral, physiological, or molecular events [16]. Different rhythms have been investigated at the organismic level ranging from milliseconds of a nerve discharge to the annual rhythms of hibernation and even longer [17].
Endogenous rhythms are inseparable parts of almost all living systems ranging from photosynthetic prokaryotic cells to higher-level organisms that act as regulators to adapt internal biological processes to external environments [18,19]. In healthy individuals, biological rhythms are interconnected and cooperate like a symphonic orchestra at all levels of the metabolic organization.
Rhythmicity is regulated by biological clocks, which are complex systems consisting of multiple oscillators that each has at least one feedback loop (Figure 1). For instance, controlling the temperature is one of the most important characteristics of biological clocks, so it is critical for a biological system to have a similar oscillation period over a wide range of temperatures [16].
Various classification systems have been used for the biological rhythms [20]; among the most important of them is the classification based on the geophysical cycle day and night: ultradian (shorter than 24 h), circadian (almost equal to 24-h daily rhythms), and infradian (longer than 24 h) rhythms [21,22].
2.1. Ultradian Rhythms
Ultradian rhythms are defined as all types of ‘short-term rhythms’ with a frequency of fewer than 24 h but commonly with periods in the range of 20 min to 6 h [23,24]. Ultradian rhythms have been detected in all types of living systems ranging from eukaryotic cells [25,26] to mammals [27] and are usually phase-coupled to circadian rhythms in healthy subjects, i.e., the ups and downs of ultradian rhythms appear each day at approximately the same time [17] and play crucial roles in the intracellular coherence [28].
Ultradian rhythms are essential for vital organ functions such as respiration, heart rate, peristaltic activity of the gastrointestinal tract, brain electrical activity [22,29], sperm physiology [30], thermoregulation [31,32,33], rapid eye movement (REM)/non-REM (NREM) sleep cycle, etc. [34]. Additionally, they play crucial roles in cell proliferation and coordination of cellular responses [35].
Besides, for the maintenance of life, an ultradian-timekeeping is required to coordinate the biochemical events and regulate the metabolism [36,37] by increasing the efficiency of signal transmission. For example, in comparison to the high level of constant hormone release, the pulsatile release is more efficient for the regulation of metabolism. Regarding the laboratory tests, ultradian rhythms such as episodic hormone secretions [38] govern many endocrine functions. For example, the episodic secretion of insulin from the beta-cell of the pancreas and gonadotropin-releasing hormone from the hypothalamus regulate blood glucose levels and reproductive functions, respectively [38].
In addition to hormones, the expression levels of various genes [38,39] and concentrations of analytes such as glucose [40,41] are under the influence of ultradian rhythms. This is especially important in the case of sampling time of the analytes whose measurement results are used for various purposes including diagnosis, screening, and monitoring of the patients and also for the determination of the reference intervals (RIs) and clinical decision limits (CDLs) of the measurands.
As described in the Section 4 and Section 5, ultradian rhythms of the measurands have great influence on the diagnosis, screening, and monitoring of patients and should be considered for the safe and valid interpretation of patients’ laboratory test results.
2.2. Circadian Rhythm
The term ‘circadian rhythm’ (from Latin “circa diem”) for the first time was proposed by Franz Haldberg in 1959 to express the daily oscillations of endogenous biological processes associated with the 24-h rotation cycle of the earth [42]. Circadian rhythm describes the 24-h oscillations of biological processes associated with the dark/light cycle and the earth’s daily rotation [19]. Various external stimuli such as light exposure and food intake play dominant roles in the regulation of circadian rhythms and the dark/light cycle is the main external synchronizer of the circadian rhythm [43]. However, it should be noted that the 24-h oscillation time can be changed slightly using different stimuli [44].
Circadian rhythms are developed prenatally and progressive maturation is observed particularly after birth [45]. The pattern of these rhythms can be categorized into two phases: activity and feeding phase and rest and fasting phase. Foods taken in the active phase provide the main substrates such as amino acids, fatty acids, and monosaccharides for energy production and synthesis of carbohydrates, lipids, and proteins. During the resting period, the stored compound are mobilized to sustain the homeostasis of the metabolism [46].
External photic and non-photic factors have a great influence on circadian behavior. Photic signals are processed by the eye and transmitted to the hypothalamic suprachiasmatic nucleus (SCN) through the retinohypothalamic tract. Melanopsin plays a crucial role in transmitting the photic signals from the eye to the SCN [47]. The SCN has a complex structure composed of approximately 10,000 neurons, which behaves as a cell-autonomous circadian oscillator, and is accepted as the master internal pacemaker, i.e., the central circadian clock [48]. The SCN has critical properties such as temperature compensation, free-running period under constant conditions, and intrinsic rhythmicity that make it an excellent biological oscillator [16].
In addition to the central clock, the presence of peripheral clocks in different organs such as the liver, skeletal muscle, heart, lungs, etc., has been reported [19]. In fact, the biological clock for circadian rhythms is present in all known cells of multicellular organisms and synchronizes the tissues with the external environment via a master circadian pacemaker located in the SCN [49,50]. The activity of peripheral clocks is synchronized by the SCN and both of them regulate the daily rhythmicity of metabolisms [51,52]. In contrast to the SCN, photic signals have little effect on peripheral clocks; however, non-photic signals have profound effects on peripheral clocks [53,54]. Although the master circadian clock in the SCN strongly influences peripheral clocks, there is evidence that peripheral clocks can also influence the master circadian clock [55,56].
Figure 1The feedback loops in a molecular clock. The complex CLOCK and BMALI1 rhythmically binds E-box and activate CCGs and other parts of the clock. The activity of CLOCK and BMALI1 is under the control of PER and CRY proteins. They translocate to the nucleus and inhibit CLOCK-BMAL1. The second loop regulates the transcription of Bmal1, which is inhibited by Reverb and activated by ROR. Reprinted from Ref. [52].
[Figure omitted. See PDF]
The molecular mechanisms behind the circadian clocks were deciphered by Michael Rosbash, Jeffrey Hall, and Michael Young, who won the Nobel Prize in Physiology or Medicine in 2017 [57].
The molecular mechanism of circadian rhythmicity is a complex procedure including transcription, translation, post-transcriptional regulation, and protein–protein interactions [49], which are regulated via both positive and negative feedback loops [58,59,60]. The feedback loops in circadian rhythms on the molecular level are summarized in Figure 1. Briefly, it is based on the complex autoregulatory transcription–translation feedback loops that control the rhythmic expression of clock-controlled genes (CCGs), leading to oscillations in numerous molecules and cellular functions. As shown in Figure 1, the complex CLOCK and BMALI1 rhythmically bind E-box and activate CCGs and other parts of the clock. On the other hand, the activity of CLOCK and BMALI1 is under the control of PER and CRY proteins. They translocate to the nucleus and inhibit CLOCK-BMAL1. These proteins are degraded within the cell and a new cycle begins every 24 h. In addition to this loop, there is a second loop that regulates the transcription of Bmal1 (Figure 1). It is inhibited by Reverb and activated by ROR. The detailed feedback loops of the molecular clock can be found in [52,61,62].
Although circadian rhythms are based on transcription–translation feedback loops, i.e., clock proteins regulate their own transcription by a negative feedback mechanism, which produce a rhythmic clock gene expression [63], it has been shown that transcription–translation is not a prerequisite for circadian oscillations [64,65,66,67]. For instance, O’Neill et al. [66] demonstrated that in red blood cell peroxiredoxins, a highly conserved family of antioxidant enzymes that play a dominant role in regulating the intracellular peroxide levels [68,69], undergo the 24-h redox cycles, i.e., the nucleus and consequently transcription–translation are not always required for circadian rhythm in humans.
The change in levels of numerous molecules within 24 h can be analyzed under the big umbrella, circadiomic [22,70]. The detailed molecular mechanism of the circadian rhythm can be found in [19].
Circadian rhythms have been observed in numerous laboratory tests including hormones [71] and various analytes such as leukocytes [72,73,74,75], electrolytes [76], trace elements [77,78], glucose [79], etc. Among the hormones, especially melatonin (N-acetyl-5-methoxytryptamine) and cortisol come to the fore. Melatonin is secreted by the pineal gland and liver [80] and has strong antioxidant activity and regulates the circadian rhythms of various physiological activities. Its biosynthesis increases at night [81] and is inhibited during daytime by the light detected by the retina [82]. Similar to melatonin, the serum concentrations of cortisol show marked variation within the day with the highest level detected in the early morning [83] (Figure 2).
2.3. Infradian Rhythms
The period of infradian rhythms is longer than the circadian rhythms with ranges from days to years. For biological systems, typical examples are menstruation, hibernation, migration, breeding, molting, etc. [85,86]. In humans, particularly in laboratory medicine, the most important infradian rhythms are observed in analytes that regulate the menstrual cycle and analytes under the influence of sunlight.
In the menstrual cycle, pituitary gonadotropins (follicle-stimulating and luteinizing hormones) and ovarian hormones (estrogen and progesterone) show infradian rhythmicity (Figure 3) [87,88]. These hormonal changes that regulate the menstrual cycle in women also have profound effects on other physiological functions, particularly on thermoregulation [89]. In comparison to the follicular phase of menstrual cycle, the core body temperature (CBT) measured in early morning is 0.3 to 0.7 °C higher in the luteal phase [89,90], which is attributed to the thermogenic effect of progesterone [91], and consequently, it can be concluded that in addition to ultradian [31,32,33,92] and circadian rhythms [33,92,93], infradian rhythms is observed in the CBT of women.
Seasonal infradian rhythms play crucial roles in body functions, particularly in metabolism, reproduction, and immune responses [95]. Recent studies have shown a seasonal variation of immunity and related analytes [95,96,97,98]. Dopico et al. [96] found seasonal expression profiles in more than 4000 protein-coding mRNAs in white blood cells and adipose tissue. They found a profound profile of pro-inflammatory transcriptomic profiles and increased levels of soluble IL-6 receptors and C-reactive proteins during the winter season. Additionally, they found that FcR-gamma-associated processes, B-cell receptor signaling, lysosomes, chemokine signaling, and phagosome were all strongly associated with winter-expressed modules. Pro-inflammatory mediators are associated with various pathological conditions including cardiovascular diseases [99,100,101,102], which is a major cause of mortality and morbidity worldwide [103].
Seasonal variation is observed in the analytes which regulate the bone mineral metabolism, particularly vitamin D, which is under the influence of the sunlight. The intensity of sunlight is not globally uniform and shows variations depending on the geographic latitudes and seasons. Depending on the intensity of the sunlight, the levels of 25-hydroxyvitamin D, calcium, and parathyroid hormone (PTH) fluctuate throughout the year. In other words, the concentration of 25-hydroxyvitamin D is higher in the summer and lower in the winter, while the PTH level shows an opposite trend. In the case of calcium, an elevated level is observed in the autumn [104].
Additionally, serum/plasma lipid levels also show seasonal variation [105,106,107,108,109,110] between winter and summer. For example, in comparison to summer, serum total cholesterol and LDL-cholesterol levels are increased in winter, but the opposite situation is observed in HDL-cholesterol levels. Detailed information regarding the seasonal variation of the lipids can be found in [111].
Moreover, infradian variations have been observed in various measurands including TSH and thyroid hormones [85,112], sperm physiology [113], urinary excretion of growth hormone [114], enzymes [115,116], etc.
It should be noted that most of the analytes that are under the influence of infradian variations such as gonadotropins [117,118,119], 25-hydroxyvitamin D, calcium, phosphorus, and parathyroid hormone also show circadian variation [120,121,122,123], as discussed in the following parts.
2.4. Interactions among Ultradian, Circadian, and Infradian Rhythms
Although it is accepted in theory that biological systems have ‘steady state’ situations and ‘homeostatic set points’ (HSPs) for the measurands, the reality is that the concentration or the activity of biomolecules oscillate as the composite rhythms consist of multiple overlapping oscillations [124] (Figure 4). However, it does not mean that the HSPs of the measurands cannot be determined. In such cases, the HSPs of the measurands can be determined using regression and other trend analysis techniques.
In comparison to circadian and infradian rhythms, the ultradian events are aperiodic and therefore they are classified as ‘episodic ultradian events’ [24]. Furthermore, while circadian and infradian rhythms are the adaptation mechanisms for predictable environmental changes, at least in theory, ultradian rhythms can be considered as the adaptation mechanisms for the unpredictable environmental changes [24]. It should be noted that the circadian clock behaves as a modulator between ultradian and infradian rhythms [85].
2.5. Disruption of Biological Rhythms
In comparison to ultradian and infradian rhythms, the disruption of circadian rhythms has been analyzed in detail. Various factors such as shift work [125], jet lag [126], social jetlag [127,128], exposure to artificial light [129], and irregular eating time [130] disrupt the circadian rhythms. Disruption of circadian rhythms lead to numerous serious health problems such as diabetes mellitus [131], atherosclerosis [132], autoimmune diseases [133], obesity [134], cancer [135], insomnia [136], etc. Unlike circadian rhythms, limited data related to the disruption of ultradian and infradian rhythms are available [137,138,139,140].
3. Biological Variation
The physiological concentration or activities of biomolecules measured in the same time interval are different in different individuals. The nature of BV is different from that of the physiological rhythms. The BV data of analytes have been derived from the data of the repeated measurements of the analytes [141] rather than the experimental models or animal studies specifically designed for this purpose. For all known analytes that have BV data in the literature, the calculated total variation of repeated measurement results was found to be higher than the analytical variation (the variation of the measurement systems), indicating the presence of additional variation, i.e., biological variation.
The sampling time for repeated measurements is crucial to obtain reliable BV data. To eliminate the effect of ultradian variation, all samples should be taken at the same time of the day, i.e., measurements of analytes should not be done by combining the samples, some taken in the morning and some in the evening. Similar care should be taken to eliminate the infradian variation of the analytes. Additionally, all samples should be analyzed in a single run to eliminate the between-run analytical variation.
For an analyte, BV has two main components: between-subject and within-subject biological variations [141]. These two main variations are also the fundamental elements of personalized laboratory medicine.
3.1. Between-Subject Biological Variation
Between-subject BV (CVG) is the variation among the individuals’ set points of the analytes (Figure 5). For an analyte, theoretically, it is accepted that each individual has a specific set point, and the concentration of the analytes varies around that set point. It should be noted that the set point of an analyte used to calculate the CVG does not need to be under strict homeostatic control. The set point can be accepted as the mean value of the repeated measurement results of the individual’s data when he/she is at a steady state i.e., the concentration of the analytes is stable. Since the mean value of the analytes is not constant in healthy and diseased individuals and in different age groups, the set point of the analytes can change with age and diseases [141,142]. Depending on the types of analytes the CVG shows a wide distribution. Based on the data given by the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) Biological Variation (BV) database, it ranges from 1.0% for sodium to 103.4% for cancer antigen 72-4 [143]. In comparison to healthy individuals, currently, there are not sufficient data to illustrate the variation in the set points of the analytes in different clinical situations.
3.2. Within-Subject Biological Variation
Within-subject BV (CVI) is accepted as the variation of an analyte around its set point (Figure 5) in an individual. In some analytes such as sodium and calcium, CVI is under strict homeostatic control, while in others, such as serum enzymes, this control is not so strict. The meta-analysis of CVI of more than 200 analytes can be found on the EFLM BV database [143]. Similar to CVG, the CVI of analytes shows a wide distribution range from 0.5% for sodium to 135% for adrenalin [143].
The EFLM BV database is a dynamic database, being updated when new data related to the BV of the analytes are available, and therefore the CVG and CVI of analytes may change when the database is updated [143].
3.3. Within-Person Biological Variation
Although, theoretically, both within-person (CVP) and CVI represent the same variation, i.e., the variation of the analytes around the homeostatic set point for an individual, actually they are not exactly the same. The difference between CVP and CVI is the source of the data used to calculate these parameters. The CVI is calculated using the results of the repeated measurements of a group of individuals (population) and therefore it is not specific to the individual, while CVP is obtained using the repeated measurement results of the individual and therefore it is specific to the individual.
3.4. Clinical Applications of Biological Variation Data
Both CVI and CVG are widely used in medical laboratory practice. The BV data have been used: (1) to calculate the index of individuality (II) to evaluate the utility of population-based reference intervals, (2) to calculate the reference change value (RCV), which can be used to make a decision regarding the significance between individual’s serial measurement test results, and (3) to calculate the bias and imprecision to set the analytical performance specification (APS) of the measurement procedure. The details on how to use the BV data for such purposes can be found in [141]. Recently, the CVI and CVP data of the analytes have been used to derive the personalized reference intervals (prRIs) of the analytes [144,145,146,147].
Using the prRIs of the analytes may increase the objective interpretation of the test results. As shown in Figure 5, each individual has their own set point and within-person BV. In other words, for an analyte, the variation of the measurands is not limited to a constant set point and a constant variation around the set point. Both the set point and the variation around the set point are individual-specific parameters. Although there are not adequate data reporting the variation of CVI and CVG, from the data of the population-based reference intervals of the analytes, it can be speculated that both CVI and CVG change with age and gender [148,149]. For an individual, this makes the variations of the analytes two-dimensional variations. One dimension is the variation of the set point and the other is the variation of CVI. To illustrate the variation of the analytes, new studies are required to measure the variation of the analytes at different ages and health statuses.
As the CVI represents the variation in the measurand around its homeostatic set point, although it is not a rule, the CVI of the measurand is expected to increase in pathological conditions related to the measurand. Various studies have reported increasing CVI of specific measurands in individuals with diseases such as diabetes mellitus, chronic kidney diseases, different type of cancers, etc. [150,151].
Figure 5Median values with range (minimum–maximum) of platelet count for individuals based on weekly samplings for 10 weeks. Dashed lines indicate the 5th and 95th percentiles, and the continuous line is the median value with 95% CIs. Reprinted from Ref. [152]. The within-person variation and the set point of each individual are different and additionally, the variation of the set point for males is lower than that for females.
[Figure omitted. See PDF]
3.5. Reliability of Biological Variation Data
Since the BV data are widely used in the calculation of critical parameters such as RCV, II, APS, and prRI, the reliability of these data is essential [153,154]. The total variation of an analyte can be accepted as the Gaussian combination of pre-analytical, analytical, and biological variation [141]. To obtain reliable BV data from the results of repeated measurements, pre-analytical variation must be minimized; however, it is not so easy in practice and needs strict protocol for pre-analytical procedures and robust statistical techniques [155]. Recently, the EFLM biological variation working group (BV-WG) set up the European Biological Variation Study (EuBIVAS) project, a multicenter study that collected samples from five different European countries using a stringent pre-analytical protocol and robust statistical techniques [155] and updated the BV data of numerous analytes [156]. Additionally, the EFLM BV task group developed the Biological Variation Critical Appraisal Checklist (BIVAC) [157] to evaluate the quality of published BV data and select the appropriate papers for meta-analysis of BV data. The meta-analysis of BV data of numerous analytes is given on the EFLM BV database [143]. In comparison to the historical database [158,159], for most of the measurands, lower values in BV data have been observed.
3.6. Biological Variation in Diseases
Disruption of physiological rhythms has been analyzed in detail [19,51,80,130,134,135,136,160] but the same situation is not the case for BVs. The BV data of measurands were mostly obtained from healthy individuals, and in comparison to healthy individuals, limited data are available for the BV of measurands in patients.
Indeed, the lack of adequate reliable data for the BV of the analytes in patients limits the usefulness of BV data in clinical practice. Patient monitoring is an important step in the evaluation of the effects/side effects of treatments and the prognosis of the diseases. For this purpose, RCV is used to evaluate the significance between consecutive measurement results. It combines the analytical and biological variations of the measurands and gives the uncertainty associated with the results of consecutive measurements. If the difference between two consecutive measurements results is higher than the RCV, it is accepted as significant, otherwise, it is considered insignificant. However, the problem is that the BV component of RCV is obtained from healthy individuals but used to evaluate the significance between the serial measurements of patients’ test results. This is only valid if there is no significant difference between the measurand CVI values of both patients and healthy individuals.
Although a BV database for patients is not available, the data of a few papers show that the BV of analytes measured in patients’ samples may be different from the BV of the analytes measured in healthy individuals. In this case, it may not be rational to use the BV values derived from the data of healthy individuals to calculate RCV to monitor patients’ tests results. Parrinello et al. [161] reported the CVI of glucose, HbA1c, fructosamine, glycated albumin, and 1,5-anhydroglucitol in both elderly diabetic and non-diabetic individuals and found elevated levels of CVI for all parameters in diabetic subjects. Within these parameters, the CVI (confidence interval, CI 95%) of glucose, 9.6 (7.3–11.8), and 1,5-anhydroglucitol, 5.7 (4.2–7.2), were found to be significantly higher in diabetic patients than the CVI of glucose, 5.3 (4.6–6.0), and 1,5-anhydroglucitol, 2.9 (2.7–3.2), in non-diabetic individuals. Similarly, Rizi et al. [40] measured the CVI of glucose, insulin, total cholesterol, LDL-cholesterol, HDL-cholesterol, and triglyceride in lean insulin-sensitive and obese insulin-resistant individuals and found elevated levels of CVI for all parameters in obese insulin-resistant individuals. However, due to the lack of CIs in the paper, the significance of the difference between the two groups could not be evaluated.
In addition to the limited number of papers that measured the BV of some analytes in patients, the reliability of these data is questionable. The individuals must be at a steady state and pre-analytical variation should be minimized, and therefore obtaining reliable BV data from patients’ samples is not an easy task.
4. Physiological Rhythms and Reference Intervals
Clinical decision based on laboratory tests is a comparative procedure and physicians need reference data to compare the laboratory test results. In daily practice, physicians usually use RIs or CDLs as the reference data for comparison [162]. Both RIs and CDLs are powerful tools for diagnosis and screening of the disease. RIs of the measurands can be obtained from the data of healthy population (popRI) or individual (prRI) but CDLs are obtained from patients’ data [162].
To derive the popRI, briefly, samples are collected from at least 120 reference individuals [163]. The concentration of the measurands is measured and after excluding the lowest and highest 2.5% of the data using appropriate statistical techniques, the remaining central 95% of the data is accepted as the popRI of the analytes. It should be noted that if partitioning is necessary due to the covariates such as age groups, sex, ethnicity, race, etc., then nx120 (n: the number of covariates) of the reference individuals should be recruited to derive the popRI of the measurands [163]. In comparison to popRI, deriving prRI is an easy procedure. It can be derived using three or more repeated measurement results of the analytes and CVI [147]. However, it is recommended to use CVP instead of CVI and in this case, five or more repeated measurements results are sufficient [144].
RIs are obtained from the samples taken in the morning period of the day usually between 8:00–11:00 a.m. However, the ultradian variation of the analyst limits the diagnostic power of the RIs derived from the measurement results of samples taken in the morning time. Since the concentration of the analytes is not constant throughout the day, the within-day variation (ultradian) can be observed in many analytes [164,165,166,167]. Therefore, the sampling time is a critical point in the interpretation of the results of laboratory tests. For example, the measurement results of samples taken at midnight should not be compared to the conventional RI. For such comparisons, the RIs derived from the samples taken within the suitable period are required. Unfortunately, such conventional RIs are not commonly available. For sampling time and reliable comparison with conventional RIs, in addition to ultradian, the circadian and infradian rhythmicity of tests should be considered. For circadian rhythms, the time of the minimum and maximum value of the analytes and additionally the amplitude of the variation, which is defined as one-half of the difference between the minimum and maximum values of the analytes, should be considered [168]. Infradian variation should be considered particularly in estimating the RIs of the analytes showing seasonal variation such as vitamin D, calcium, PTH, total cholesterol, LDL-cholesterol, HDL-cholesterol, etc., and monthly variation such as hormones regulating the menstrual cycle. In clinical practice, population-based RIs (popRI) based on monthly variation is available for hormones [169,170,171] but unfortunately, the popRI based on seasonal data are not common, particularly in routine practice. RIs based on seasonal data for both population and individuals may facilitate the safe and valid interpretation of laboratory test results.
5. Physiological Rhythms and Reference Change Value
Although RCV is a powerful tool for monitoring personal serial measurement results [172], its clinical significance has been criticized [173]. Due to ultradian rhythms of the measurands, RCV should not be used to evaluate the significance between consecutive measurement results of samples taken at different times of the day. For reliable comparisons, the sampling time of the consecutive measurement results should be considered. It should be noted that the CVI used to calculate the RCV is obtained from the data of samples usually taken in the morning period of different days and therefore it should be applied to the measurements results obtained from the samples taken in the morning period of different days. Otherwise, circadian and ultradian variations cannot be eliminated and false positive results may be reported. Moreover, in some cases, sampling at the same time for consecutive days may not be sufficient to eliminate variations other than CVI. This can be encountered in the calculation of the RCV for the tests which show infradian variations such as gonadotropic and ovarian hormones, lipids, vitamin D, calcium, etc., especially if the time interval between consecutive measurements approximates the infradian periods of the test.
6. Physiological Rhythms and Chronotypes
Chronotype, or diurnal preference, reflects an individual’s preferred time of the day for the sleep/wake or rest–active cycle [174,175]. Different sleep/wake cycle patterns exist in humans and mainly three different chronotypes can be distinguished: morning types (M-types), evening types (E-types), and neither types (N-types). M-types and E-types are also subdivided into moderate and extreme types [176]. N-types have no circadian preference and can be considered as an intermediate type between M and E-types [176,177]. In comparison to M-types, the E-types have less physical activity and more sedentary time [177]. It should be noted that although sleep is an important dimension, chronotype has multiple dimensions and other factors such as environmental and social influences shape the chronotypes of individuals [178]. The individuals’ chronotypes can be determined using a Morningness–Eveningness Questionnaire [179].
The timing of food intake, which varies depending on chronotypes, has a profound effect on the regulation of biological clocks. Inappropriate timing of food intake (for example during the night) can desynchronize biological clocks and cause adverse health outcomes [180]. It has been shown that more evening chronotypes are associated with obesity [181], type 2 diabetes [182], hypertension [182], mental health issues [183], etc.
The hormonal profiles of M- and E-types are different. For example, the melatonin profile of M-types differs from E-types [176] and therefore melatonin level can be used as a biomarker for chronotypes [184,185]. In comparison to E-types, the onset, acrophase, and offset of the melatonin profiles occur approximately 3 h earlier in M-types and therefore serum melatonin levels measured particularly around 9:00 a.m. can be used as an indicator to differentiate E-types from M-types since M-types have a lower melatonin level at 9:00 than E-types do [185].
Differences have been observed between the laboratory test results of different chronotypes. Vera et al. [186] analyzed the lifestyle, chronotypes, and metabolic syndrome and found that in comparison to M-types, triglyceride and insulin levels and Homeostatic Model Assessment—Insulin Resistance (HOMA–IR) were higher but HDL-cholesterol was lower in E-types. On the other hand, Lucasen et al. [187] found elevated adrenocorticotropic hormone (ACTH) and epinephrine levels in E-types compared to M- and I-types but the elevation of other analytes such as glucose, triglyceride, HDL-cholesterol, and LDL-cholesterol were not significantly different.
In several chronic pathological conditions particularly cancers, sleep–wake or rest–activity rhythm abnormalities, which are associated with chronotypes, are observed [188]. The rest–activity and cortisol circadian rhythms have been associated with the mortality rates of different cancers including renal [189], lung [190], colorectal [191], and breast cancers [192,193]. Furthermore, the hormonal imbalance between leptin (reduced) and ghrelin (elevated) levels observed in chronotypes with sleep-deprived individuals increases caloric intake, decreases energy expenditure, and leads to weight gain and cardiovascular disease [194,195,196,197]. Additionally, chronotherapy, i.e., optimal timing of treatment could increase the drug efficacy and decrease the side effects of chemo and other therapeutic interventions [198].
7. 3P Medicine and Variations
As mentioned previously, both systematic and random variations (or rhythmic and non-rhythmic variations) could affect the prediction and prevention of disease and find a way for disease treatment based on the characteristics of the patient. They could significantly reduce the duration of treatment, suggest a more effective therapeutic method, and provide a more comfortable situation for the patient.
Determining the pattern of physiological rhythms or circadian phenotype for each individual is a major challenge in personalized medicine [199]. A detailed questionnaire including environmental factors such as the duration of the light/dark cycle, exposure to artificial light, information about the individual’s daily habits, nutritional status, physical activities, monitoring of physiological parameters such as blood pressure, etc., will increase the efficacy of targeted therapy and facilitate the planning of personalized treatment. Additionally, algorithms capable of integrating circadian phenotype, personalized RIs, and clinical decision limits will enhance the efficacy of chronobiology in 3P medicine.
A lifestyle that is compatible with physiological rhythms can prevent various diseases including diabetes mellitus, atherosclerosis, autoimmune diseases, obesity, cancer, insomnia, etc. [19].
People are exposed to artificial light from different sources at different degrees and durations, which has a negative impact on circadian rhythms by suppressing melatonin and phase-shifting the biological clock [200]. Consequently, in the past 50 years, the quality and average sleep duration declined, which is harmful to the health of individuals [200].
All cells in the human body have molecular clocks that oscillate regularly by binding transcription factors to various parts of the genome [201]. Transcription factors binding to numerous genes change the level of different protein and metabolite and posttranslational modifications. Therefore, disruption of circadian rhythms has been linked to numerous diseases such as cancers, diabetes mellitus, obesity, metabolic syndrome, etc.
Chronopharmacology and chronomedicine will increase the therapeutic effect of drugs and treatment procedures for personalized medicine. It has been shown that the target of numerous drugs shows cyclic gene expression [199,202] and for example, the efficacy of some antihypertensive drugs such as angiotensin II receptor antagonists and Ca channel blockers was found to be higher in the evening time [199,203].
No two individuals are identical and this is correct for their chronotypes. Environmental factors such as the duration of the light/dark cycle, exercises, and diet can change tissue specific rhythms [199]. The benefit of physical activity [204] and food intake [205] vary according to the day. Chrononutrition has a great influence on the metabolic and endocrine pathways, which regulate the homeostasis of the organism, and additionally microbiota play a critical role in this interaction [205]. Circadian clock manages the link between homeostasis and nutrition. Overnutrition disrupts circadian rhythms and obesity cause remodeling of circadian activity [206]. Targeted therapy to molecules at their peak expression time may increase the efficacy of treatment. For example, Guan et al. [206] have shown that pharmacological targeting of PPAR-α (a regulator of lipid metabolism) at its peak expression time lowered lipid accumulation in the liver effectively.
The effect on circadian rhythms is not limited to endocrine and metabolic pathways and the diseases linked to these pathways such as diabetes mellitus, obesity, metabolic syndrome, etc. Since each cell has its own molecular clock, the effect of rhythmic expression of genes can be observed in almost all diseases. For instance, it is revealed that the circadian rhythm could have participated in the duration of bone formation in periodicity-regulated orthodontic tooth movement by manipulating the genes related to the circadian rhythm. Indeed, more effective personalized care could be suggested for maxillofacial surgery and other types of plastic surgery via recognizing the specific environmental conditions and individual genetic and circadian rhythms that could play critical roles in these situations by affecting reconstruction of the soft/hard tissue in a more efficient way [207]. Moreover, variants of the circadian gene in cooperation with the sleep pattern have a relationship with the pathology, development, progression, and aggressiveness of different types of cancer [208,209]. Besides, circadian rhythms are useful for the treatment, prediction, and prevention of several physical and mental diseases including cardiovascular disease, myocardial ischemia, neurodegenerative disease, body microbiota, sleep–wake disorder, etc. [210,211,212,213].
In addition to the circadian rhythm, the application of other types of variations in 3P medicine was also confirmed; however, most of the data are related to the circadian rhythms. For example, evidence show the effects of the ultradian rhythms on heart rate variability, gene expression, locomotor activity, and body temperature [214]. Similarly, infradian rhythms have been associated with reproduction, sperm physiology, telomer length, aging, etc., and the coordination of physiological rhythms are essentials for healthy behavior and memory functions [30,215,216]. Seasonal variations have been observed in telomer lengths [217,218,219] and shortening of telomere length is observed in cellular senescence and accepted as a biomarker for aging [220,221].
Infradian rhythms were observed in testis function [222], semen quality [223] and some parameters of sperm physiology such as acrosome reaction (a 12 months cycle) and chemotaxis (a 6 month cycle) [30]. The cyclicity of the biological clock of somatic cells is regulated by specific genes [224]. However, the spermatozoans are transcriptionally inactive cells and therefore the presence of some molecules such as melatonin in the seminal plasma may regulate the variations observed in sperm physiology [225].
8. Conclusions
Variations are known as one of the inseparable parts of human metabolism that is classified into two main categories: physiological rhythms and biological variations. Physiological rhythms are crucial for the metabolic adaptation of the organism to external changes, and their disruption leads to serious clinical situations such as cancer, neurodegenerative diseases, insomnia, etc. Unlike physiological rhythms, BV is the random variation of the analytes around its set point (CVI) and the variation of set points among individuals (CVG). The relation between the disruption of BV and disease has not been analyzed in detail yet; however, in some diseases, the elevated level of CVI has been reported. All analytes measured in medical laboratories from patients’ samples are to some extent under the influence of physiological rhythms. Contrary to physiological rhythms, the BV of the analytes and their potential benefits in clinical practice are not adequately known among clinicians; however, the BV data of the measurands have been used in medical laboratory practice to calculate RCV, II, APS, and prRIs of the analytes. Correct estimations of BV data require detailed knowledge of the physiological rhythms of the analytes. Consequently, for the safe and valid interpretation of laboratory test results, both physiological rhythms and BV of the measurands should be considered simultaneously. These data are useful for effective management of predicting, preventing, and personalizing medicine.
Writing—original draft preparation, A.C.; validation, A.C. and A.Z. (Ali Zarrabi); writing—review and editing, A.C., A.Z. (Ali Zarrabi) and A.Z. (Atefeh Zarepour). All authors have read and agreed to the published version of the manuscript.
Not applicable.
Not applicable.
Not applicable.
The authors declare no conflict of interest.
Footnotes
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![View Image - Figure 2. Within-day variations observed in the body temperature and melatonin and cortisol concentrations; reprinted from Ref. [84]. The within-day variation in body temperature is in the opposite direction to the variation observed in melatonin and cortisol.](https://media.proquest.com/media/hms/THUM/2/gBBGR?_a=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&_s=ldmR%2Ff4ZtBAGOzhUZsyaCfUMMC4%3D "View Image - Figure 2. Within-day variations observed in the body temperature and melatonin and cortisol concentrations; reprinted from Ref. [84]. The within-day variation in body temperature is in the opposite direction to the variation observed in melatonin and cortisol.")
Enlarge this image.Figure 2. Within-day variations observed in the body temperature and melatonin and cortisol concentrations; reprinted from Ref. [84]. The within-day variation in body temperature is in the opposite direction to the variation observed in melatonin and cortisol.
![View Image - Figure 3. Hormonal changes observed during (A) a normal menstrual cycle, (B) when taking an oral contraceptive containing both progesterone and estrogen, and (C) in the years before and after menopause. Various factors such as pharmacological agents, diseases, or age (a physiological factor) can change the pattern of infradian variations in the analytes; reprinted from Ref. [94].](https://media.proquest.com/media/hms/THUM/4/gBBGR?_a=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&_s=MDvvbTv%2BH99oLXo3NFm0Q5cB9fg%3D "View Image - Figure 3. Hormonal changes observed during (A) a normal menstrual cycle, (B) when taking an oral contraceptive containing both progesterone and estrogen, and (C) in the years before and after menopause. Various factors such as pharmacological agents, diseases, or age (a physiological factor) can change the pattern of infradian variations in the analytes; reprinted from Ref. [94].")
Enlarge this image.Figure 3. Hormonal changes observed during (A) a normal menstrual cycle, (B) when taking an oral contraceptive containing both progesterone and estrogen, and (C) in the years before and after menopause. Various factors such as pharmacological agents, diseases, or age (a physiological factor) can change the pattern of infradian variations in the analytes; reprinted from Ref. [94].
![View Image - Figure 4. Some metabolic and physiological activities under the influence of ultradian, circadian, and infradian rhythms; reprinted from Ref. [85]. Some analytes and physiological events are under the influence of more than one variation. For example, cortisol secretion has both circadian and ultradian rhythms and the menstrual cycle has infradian and circadian rhythms.](https://media.proquest.com/media/hms/THUM/6/gBBGR?_a=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&_s=2R8WJ0E0c343xh3IIgi3vdEd%2FiI%3D "View Image - Figure 4. Some metabolic and physiological activities under the influence of ultradian, circadian, and infradian rhythms; reprinted from Ref. [85]. Some analytes and physiological events are under the influence of more than one variation. For example, cortisol secretion has both circadian and ultradian rhythms and the menstrual cycle has infradian and circadian rhythms.")
Enlarge this image.Figure 4. Some metabolic and physiological activities under the influence of ultradian, circadian, and infradian rhythms; reprinted from Ref. [85]. Some analytes and physiological events are under the influence of more than one variation. For example, cortisol secretion has both circadian and ultradian rhythms and the menstrual cycle has infradian and circadian rhythms.
References
1. Ade, P.A.; Aghanim, N.; Arnaud, M.; Ashdown, M.; Aumont, J.; Baccigalupi, C.; Banday, A.J.; Barreiro, R.B.; Bartlett, J.G.; Bartolo, N. et al. Planck 2015 results-xiii. cosmological parameters. Astron. Astrophys.; 2016; 594, A13.
2. Graham, D.W. Heraclitus: Flux, order, and knowledge. The Oxford Handbook of Presocratic Philosophy; Curd, P.; Graham, D.W. Oxford University Press: Oxford, UK, 2008; pp. 169-188.
3. Rosen, I.M. Change is the Only Constant in Life (and in Sleep Medicine). J. Clin. Sleep Med.; 2018; 14, pp. 1025-1030. [DOI: https://dx.doi.org/10.5664/jcsm.7174] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/29852896]
4. Tu, Y.; Rappel, W.J. Adaptation in Living Systems. Annu. Rev. Condens. Matter Phys.; 2018; 9, pp. 183-205. [DOI: https://dx.doi.org/10.1146/annurev-conmatphys-033117-054046] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/30057689]
5. Nakajima, T. Biologically inspired information theory: Adaptation through construction of external reality models by living systems. Prog. Biophys. Mol. Biol.; 2015; 119, pp. 634-648. [DOI: https://dx.doi.org/10.1016/j.pbiomolbio.2015.07.008]
6. Chatterjee, A.; Georgiev, G.; Iannacchione, G. Aging and efficiency in living systems: Complexity, adaptation and self-organization. Mech. Ageing Dev.; 2017; 163, pp. 2-7. [DOI: https://dx.doi.org/10.1016/j.mad.2017.02.009]
7. Buijink, M.R.; Olde Engberink, A.H.; Wit, C.B.; Almog, A.; Meijer, J.H.; Rohling, J.H.; Michel, S. Aging Affects the Capacity of Photoperiodic Adaptation Downstream from the Central Molecular Clock. J. Biol. Rhythm.; 2020; 35, pp. 167-179. [DOI: https://dx.doi.org/10.1177/0748730419900867]
8. Saderi, N.; Escobar, C.; Salgado-Delgado, R. Alteration of biological rhythms causes metabolic diseases and obesity. Rev. Neurol.; 2013; 57, pp. 71-78.
9. Smith, S.K.; Musiek, E.S. Impact of circadian and diurnal rhythms on cellular metabolic function and neurodegenerative diseases. Int. Rev. Neurobiol.; 2020; 154, pp. 393-412.
10. Tong, X.; Yin, L. Circadian Rhythms in Liver Physiology and Liver Diseases. Compr. Physiol.; 2013; 3, pp. 917-940.
11. Feriel, J.; Tchipeva, D.; Depasse, F. Effects of circadian variation, lifestyle and environment on hematological parameters: A narrative review. Int. J. Lab. Hematol.; 2021; 43, pp. 917-926. [DOI: https://dx.doi.org/10.1111/ijlh.13590]
12. Hernández-García, J.; Navas-Carrillo, D.; Orenes-Piñero, E. Alterations of circadian rhythms and their impact on obesity, metabolic syndrome and cardiovascular diseases. Crit. Rev. Food Sci. Nutr.; 2020; 60, pp. 1038-1047. [DOI: https://dx.doi.org/10.1080/10408398.2018.1556579] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/30633544]
13. Seifalian, A.; Hart, A. Circadian Rhythms: Will It Revolutionise the Management of Diseases?. J. Lifestyle Med.; 2019; 9, pp. 1-11. [DOI: https://dx.doi.org/10.15280/jlm.2019.9.1.1] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/30918828]
14. Prendergast, B.J.; Zucker, I. Ultradian rhythms in mammalian physiology and behavior. Curr. Opin. Neurobiol.; 2016; 40, pp. 150-154. [DOI: https://dx.doi.org/10.1016/j.conb.2016.07.011] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/27568859]
15. Laerum, O.D.; Smaaland, R. Circadian and infradian aspects of the cell cycle: From past to future. Chronobiologia; 1989; 16, pp. 441-453.
16. Lamont, E.W.; Amir, S. Circadian and ultradian clocks/rhythms. Encyclopedia of Behavioral Neuroscience; 2nd ed. Della Sala, S. Academic Press: London, UK, 2017; pp. 746-751.
17. Moser, M.; Penter, R.; Fruehwirth, M.; Kenner, T. Why life oscillates-Biological rhythms and health. Proceedings of the 2006 International Conference of the IEEE Engineering in Medicine and Biology Society; New York, NY, USA, 30 August–3 September 2006.
18. Harmer, S.L.; Panda, S.; Kay, S.A. Molecular Bases of Circadian Rhythms. Annu. Rev. Cell Dev. Biol.; 2001; 17, pp. 215-253. [DOI: https://dx.doi.org/10.1146/annurev.cellbio.17.1.215]
19. Fagiani, F.; Di Marino, D.; Romagnoli, A.; Travelli, C.; Voltan, D.; Di Cesare Mannelli, L.; Racchi, M.; Govoni, S.; Lanni, C. Molecular regulations of circadian rhythm and implications for physiology and diseases. Signal Transduct. Target. Ther.; 2022; 7, 41. [DOI: https://dx.doi.org/10.1038/s41392-022-00899-y]
20. Demas, G.E.; Kriegsfeld, L.J. Biological Rhythms. Encycl. Endocr. Dis.; 2004; 7, pp. 345-351.
21. Golombek, D.A.; Bussi, I.L.; Agostino, P.V. Minutes, days and years: Molecular interactions among different scales of biological timing. Philos. Trans. R. Soc. B Biol. Sci.; 2014; 369, 20120465. [DOI: https://dx.doi.org/10.1098/rstb.2012.0465]
22. Tabibzadeh, S. CircadiOmic medicine and aging. Ageing Res. Rev.; 2021; 71, 101424. [DOI: https://dx.doi.org/10.1016/j.arr.2021.101424]
23. Aschoff, J. Aschoff, J. A Survey on Biological Rhythms. Biological Rhythms; 1st ed. Aschoff, J. Springer: Boston, MA, USA, 1981; pp. 3-10.
24. Goh, G.H.; Maloney, S.K.; Mark, P.J.; Blache, D. Episodic Ultradian Events—Ultradian Rhythms. Biology; 2019; 8, 15. [DOI: https://dx.doi.org/10.3390/biology8010015]
25. Kippert, F.; Hunt, P. Ultradian clocks in eukaryotic microbes: From behavioural observation to functional genomics. Bioessays; 2000; 22, pp. 16-22. [DOI: https://dx.doi.org/10.1002/(SICI)1521-1878(200001)22:1<16::AID-BIES5>3.0.CO;2-1]
26. Kippert, F. The ultradian clocks of eukaryotic microbes: Timekeeping devices displaying a homeostasis of the period. Chronobiol. Int.; 1997; 14, pp. 469-479. [DOI: https://dx.doi.org/10.3109/07420529709001469] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/9298283]
27. Brodsky, V.Y. Circahoralian (Ultradian) metabolic rhythms. Biochemistry; 2014; 79, pp. 483-495. [DOI: https://dx.doi.org/10.1134/S0006297914060017]
28. Lloyd, D.; Murray, D.B. Ultradian metronome: Timekeeper for orchestration of cellular coherence. Trends Biochem. Sci.; 2005; 30, pp. 373-377. [DOI: https://dx.doi.org/10.1016/j.tibs.2005.05.005]
29. López-Otín, C.; Kroemer, G. Hallmarks of Health. Cell; 2021; 184, pp. 33-63. [DOI: https://dx.doi.org/10.1016/j.cell.2020.11.034] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/33340459]
30. Moreno-Irusta, A.; Kembro, J.M.; Domínguez, E.M.; Matamoros-Volante, A.; Gallea, M.N.; Molina, R.; Guidobaldi, H.A.; Treviño, C.L.; Figueras, M.J.; Babini, A. et al. Sperm physiology varies according to ultradian and infradian rhythms. Sci. Rep.; 2019; 9, pp. 1-12. [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/30979936][DOI: https://dx.doi.org/10.1038/s41598-019-42430-4]
31. Blessing, W.W. Thermoregulation and the ultradian basic rest-activity cycle. Handb. Clin. Neurol.; 2018; 156, pp. 367-375. [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/30454601]
32. Lindsley, G.; Dowse, H.B.; Burgoon, P.W.; Kolka, M.A.; Stephenson, L.A. A persistent circhoral ultradian rhythm is identified in human core temperature. Chronobiol. Int.; 1999; 16, pp. 69-78. [DOI: https://dx.doi.org/10.3109/07420529908998713]
33. Morf, J.; Schibler, U. Body temperature cycles: Gatekeepers of circadian clocks. Cell Cycle; 2013; 12, pp. 539-540. [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/23343768][DOI: https://dx.doi.org/10.4161/cc.23670]
34. Moszczynski, A.; Murray, B.J. Neurobiological aspects of sleep physiology. Neurol. Clin.; 2012; 30, pp. 963-985. [DOI: https://dx.doi.org/10.1016/j.ncl.2012.08.001]
35. Isomura, A.; Kageyama, R. Ultradian oscillations and pulses: Coordinating cellular responses and cell fate decisions. Development; 2014; 141, pp. 3627-3636. [DOI: https://dx.doi.org/10.1242/dev.104497] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/25249457]
36. Gilbert, D.; Lloyd, D. The living cell: A complex autodynamic multi-oscillator system?. Cell Biol. Int.; 2000; 24, pp. 569-580. [DOI: https://dx.doi.org/10.1006/cbir.2000.0571] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/10924230]
37. Lloyd, D.; Aon, M.A.; Cortassa, S. Why homeodynamics, not homeostasis?. Sci. World J.; 2001; 1, pp. 133-145. [DOI: https://dx.doi.org/10.1100/tsw.2001.20]
38. Nunemaker, C.S.; Satin, L.S. Episodic hormone secretion: A comparison of the basis of pulsatile secretion of insulin and GnRH. Endocrine; 2014; 47, pp. 49-63. [DOI: https://dx.doi.org/10.1007/s12020-014-0212-3]
39. Nuñez, L.; Faught, W.J.; Frawley, L.S. Episodic gonadotropin-releasing hormone gene expression revealed by dynamic monitoring of luciferase reporter activity in single, living neurons. Proc. Natl. Acad. Sci. USA; 1998; 95, pp. 9648-9653. [DOI: https://dx.doi.org/10.1073/pnas.95.16.9648]
40. Rizi, E.P.; Baig, S.; Toh, S.A.; Loh, T.P.; Khoo, C.M. Biological variation of glucose, insulin and lipids in lean, insulin-sensitive and obese, insulin-resistant Chinese males without diabetes. Pathology; 2016; 48, pp. 510-512. [DOI: https://dx.doi.org/10.1016/j.pathol.2016.04.009] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/27311872]
41. Simon, C. Ultradian pulsatility of plasma glucose and insulin secretion rate: Circadian and sleep modulation. Horm. Res.; 1998; 49, pp. 185-190. [DOI: https://dx.doi.org/10.1159/000023169]
42. Halberg, F. Physiologic 24-hour periodicity; general and procedural considerations with reference to the adrenal cycle. Int. Z. Vitam. Beih; 1959; 10, pp. 225-296.
43. Skeldon, A.C.; Phillips, A.J.K.; Dijk, D.J. The effects of self-selected light-dark cycles and social constraints on human sleep and circadian timing: A modeling approach. Sci. Rep.; 2017; 7, pp. 1-14. [DOI: https://dx.doi.org/10.1038/srep45158]
44. Scheer, F.A.; Wright, K.P., Jr.; Kronauer, R.E.; Czeisler, C.A. Plasticity of the intrinsic period of the human circadian timing system. PLoS ONE; 2007; 2, 721. [DOI: https://dx.doi.org/10.1371/journal.pone.0000721]
45. Escobar, C.; Rojas-Granados, A.; Angeles-Castellanos, M. Development of the circadian system and relevance of periodic signals for neonatal development. Handb. Clin. Neurol.; 2021; 179, pp. 249-258. [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/34225966]
46. Peek, C.B.; Ramsey, K.M.; Marcheva, B.; Bass, J. Nutrient sensing and the circadian clock. Trends Endocrinol. Metab.; 2012; 23, pp. 312-318. [DOI: https://dx.doi.org/10.1016/j.tem.2012.02.003]
47. Hughes, S.; Jagannath, A.; Hankins, M.W.; Foster, R.G.; Peirson, S.N. Photic Regulation of Clock Systems. Methods Enzym.; 2015; 552, pp. 125-143.
48. Patton, A.P.; Hastings, M.H. The suprachiasmatic nucleus. Curr. Biol.; 2018; 28, pp. R816-R822. [DOI: https://dx.doi.org/10.1016/j.cub.2018.06.052] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/30086310]
49. Rosenwasser, A.M.; Turek, F.W. Neurobiology of Circadian Rhythm Regulation. Sleep Med. Clin.; 2015; 10, pp. 403-412. [DOI: https://dx.doi.org/10.1016/j.jsmc.2015.08.003]
50. Pacheco-Bernal, I.; Becerril-Pérez, F.; Aguilar-Arnal, L. Circadian rhythms in the three-dimensional genome: Implications of chromatin interactions for cyclic transcription. Clin. Epigenet.; 2019; 11, 79. [DOI: https://dx.doi.org/10.1186/s13148-019-0677-2]
51. Kalsbeek, A.; La Fleur, S.; Fliers, E. Circadian control of glucose metabolism. Mol. Metab.; 2014; 3, pp. 372-383. [DOI: https://dx.doi.org/10.1016/j.molmet.2014.03.002]
52. Pickel, L.; Sung, H.K. Feeding Rhythms and the Circadian Regulation of Metabolism. Front. Nutr.; 2020; 7, 39. [DOI: https://dx.doi.org/10.3389/fnut.2020.00039]
53. Zhang, Z.; Shui, G.; Li, M.D. Time to eat reveals the hierarchy of peripheral clocks. Trends Cell Biol.; 2021; 31, pp. 869-872. [DOI: https://dx.doi.org/10.1016/j.tcb.2021.08.003]
54. Kuehn, B.M. Resetting the Circadian Clock Might Boost Metabolic Health. JAMA; 2017; 317, pp. 1303-1305. [DOI: https://dx.doi.org/10.1001/jama.2017.0653]
55. Astiz, M.; Heyde, I.; Oster, H. Mechanisms of communication in the mammalian circadian timing system. Int. J. Mol. Sci.; 2019; 20, 343. [DOI: https://dx.doi.org/10.3390/ijms20020343] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/30650649]
56. Lamont, E.W.; Renteria Diaz, L.; Barry-Shaw, J.; Stewart, J.; Amir, S. Daily restricted feeding rescues a rhythm of period2 expression in the arrhythmic suprachiasmatic nucleus. Neuroscience; 2005; 132, pp. 245-248. [DOI: https://dx.doi.org/10.1016/j.neuroscience.2005.01.029]
57. Ledford, H.; Callaway, E. Circadian clocks scoop Nobel prize. Nature; 2017; 550, 18. [DOI: https://dx.doi.org/10.1038/nature.2017.22911]
58. Lande-Diner, L.; Boyault, C.; Kim, J.Y.; Weitz, C.J. A positièe feedback loop links circadian clock factor CLOCK-BMAL1 to the basic transcriptional machinery. Proc. Natl. Acad. Sci. USA; 2013; 110, pp. 16021-16026. [DOI: https://dx.doi.org/10.1073/pnas.1305980110] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/24043798]
59. Pett, J.P.; Korenčič, A.; Wesener, F.; Kramer, A.; Herzel, H. Feedback Loops of the Mammalian Circadian Clock Constitute Repressilator. PLoS Comput. Biol.; 2016; 12, e1005266. [DOI: https://dx.doi.org/10.1371/journal.pcbi.1005266] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/27942033]
60. Brown, L.S.; Doyle, F.J. A dual-feedback loop model of the mammalian circadian clock for multi-input control of circadian phase. PLoS Comput. Biol.; 2020; 16, e1008459. [DOI: https://dx.doi.org/10.1371/journal.pcbi.1008459] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/33226977]
61. Gaucher, J.; Montellier, E.; Sassone-Corsi, P. Molecular Cogs: Interplay between Circadian Clock and Cell Cycle. Trends Cell Biol.; 2018; 28, pp. 368-379. [DOI: https://dx.doi.org/10.1016/j.tcb.2018.01.006]
62. Takahashi, J.S. Transcriptional architecture of the mammalian circadian clock. Nat. Rev. Genet.; 2016; 18, pp. 164-179. [DOI: https://dx.doi.org/10.1038/nrg.2016.150]
63. Reddy, A.B.; O’Neill, J.S. Healthy clocks, healthy body, healthy mind. Trends Cell Biol.; 2010; 20, pp. 36-44. [DOI: https://dx.doi.org/10.1016/j.tcb.2009.10.005]
64. Tomita, J.; Nakajima, M.; Kondo, T.; Iwasaki, H. No Transcription-Translation Feedback in Circadian Rhythm of KaiC Phosphorylation. Science; 2005; 307, pp. 251-254. [DOI: https://dx.doi.org/10.1126/science.1102540]
65. Lakin-Thomas, P.L. Transcriptional feedback oscillators: Maybe, maybe not…. J. Biol. Rhythm.; 2006; 21, pp. 83-92. [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/16603673][DOI: https://dx.doi.org/10.1177/0748730405286102]
66. O’Neill, J.S.; Reddy, A.B. Circadian clocks in human red blood cells. Nature; 2011; 469, pp. 498-503. [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/21270888][DOI: https://dx.doi.org/10.1038/nature09702]
67. Beale, A.D.; Labeed, F.H.; Kitcatt, S.J.; O’Neill, J.S. Detecting Circadian Rhythms in Human Red Blood Cells by Dielectrophoresis. Methods Mol. Biol.; 2022; 2482, pp. 255-264. [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/35610432]
68. Hall, A.; Karplus, P.A.; Poole, L.B. Typical 2-Cys peroxiredoxins–structures, mechanisms and functions. FEBS J.; 2009; 276, pp. 2469-2477. [DOI: https://dx.doi.org/10.1111/j.1742-4658.2009.06985.x]
69. Perkins, A.; Nelson, K.J.; Parsonage, D.; Poole, L.B.; Karplus, P.A. Peroxiredoxins: Guardians against oxidative stress and modulators of peroxide signaling. Trends Biochem. Sci.; 2015; 40, pp. 435-445. [DOI: https://dx.doi.org/10.1016/j.tibs.2015.05.001]
70. Ceglia, N.; Liu, Y.; Chen, S.; Agostinelli, F.; Eckel-Mahan, K.; Sassone-Corsi, P.; Baldi, P. CircadiOmics: Circadian omic web portal. Nucleic Acids Res.; 2018; 46, pp. W157-W162. [DOI: https://dx.doi.org/10.1093/nar/gky441]
71. Haus, E. Chronobiology in the endocrine system. Adv. Drug Deliv. Rev.; 2007; 59, pp. 985-1014. [DOI: https://dx.doi.org/10.1016/j.addr.2007.01.001]
72. Loef, B.; Nanlohy, N.M.; Jacobi, R.H.; van de Ven, C.; Mariman, R.; van der Beek, A.J.; Proper, K.I.; van Baarle, D. Immunological effects of shift work in healthcare workers. Sci. Rep.; 2019; 9, pp. 1-11. [DOI: https://dx.doi.org/10.1038/s41598-019-54816-5]
73. Souto Filho, J.T.D.; Portugal, R.D.; Nucci, M. Effect of circadian variation on neutrophil mobilization to the peripheral blood in benign constitutional neutropenia. Exp. Hematol.; 2019; 69, pp. 22-26. [DOI: https://dx.doi.org/10.1016/j.exphem.2018.10.007]
74. Ackermann, K.; Revell, V.L.; Lao, O.; Rombouts, E.J.; Skene, D.J.; Kayser, M. Diurnal rhythms in blood cell populations and the effect of acute sleep deprivation in healthy young men. Sleep; 2012; 35, pp. 933-940. [DOI: https://dx.doi.org/10.5665/sleep.1954]
75. Sennels, H.P.; Jørgensen, H.L.; Hansen, A.L.S.; Goetze, J.P.; Fahrenkrug, J. Diurnal variation of hematology parameters in healthy young males: The Bispebjerg study of diurnal variations. Scand. J. Clin. Lab. Investig.; 2011; 71, pp. 532-541. [DOI: https://dx.doi.org/10.3109/00365513.2011.602422] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/21988588]
76. Zhang, D.; Pollock, D.M. Diurnal Regulation of Renal Electrolyte Excretion: The Role of Paracrine Factors. Annu. Rev. Physiol.; 2020; 82, pp. 343-363. [DOI: https://dx.doi.org/10.1146/annurev-physiol-021119-034446] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/31635525]
77. Hongo, T.; Suzuki, T.; Ishida, H.; Kabuto, M.; Neriishi, K. Diurnal variation of plasma minerals and trace elements in a group of Japanese male adults. J. Nutr. Sci. Vitaminol.; 1993; 39, pp. 33-46. [DOI: https://dx.doi.org/10.3177/jnsv.39.33]
78. Scales, W.E.; Vander, A.J.; Brown, M.B.; Kluger, M.J. Human circadian rhythms in temperature, trace metals, and blood variables. J. Appl. Physiol.; 1988; 65, pp. 1840-1846. [DOI: https://dx.doi.org/10.1152/jappl.1988.65.4.1840]
79. Zhao, L.; Hutchison, A.T.; Heilbronn, L.K. Carbohydrate intake and circadian synchronicity in the regulation of glucose homeostasis. Curr. Opin. Clin. Nutr. Metab. Care; 2021; 24, pp. 342-348. [DOI: https://dx.doi.org/10.1097/MCO.0000000000000756] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/33883418]
80. Sato, K.; Meng, F.; Francis, H.; Wu, N.; Chen, L.; Kennedy, L.; Zhou, T.; Franchitto, A.; Onori, P.; Gaudio, E. et al. Melatonin and circadian rhythms in liver diseases: Functional roles and potential therapies. J. Pineal Res.; 2020; 68, e12639. [DOI: https://dx.doi.org/10.1111/jpi.12639] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/32061110]
81. Gnocchi, D.; Bruscalupi, G. Circadian Rhythms and Hormonal Homeostasis: Pathophysiological Implications. Biology; 2017; 6, 10. [DOI: https://dx.doi.org/10.3390/biology6010010]
82. Nava Zisapel, C.; Zisapel, N. New perspectives on the role of melatonin in human sleep, circadian rhythms and their regulation. Br. J. Pharmacol.; 2018; 175, pp. 3190-3199. [DOI: https://dx.doi.org/10.1111/bph.14116]
83. Chan, S.; Debono, M. Replication of cortisol circadian rhythm: New advances in hydrocortisone replacement therapy. Ther. Adv. Endocrinol. Metab.; 2010; 1, pp. 129-138. [DOI: https://dx.doi.org/10.1177/2042018810380214]
84. Hickie, I.B.; Naismith, S.L.; Robillard, R.; Scott, E.M.; Hermens, D.F. Manipulating the sleep-wake cycle and circadian rhythms to improve clinical management of major depression. BMC Med.; 2013; 11, 79. [DOI: https://dx.doi.org/10.1186/1741-7015-11-79]
85. Laje, R.; Agostino, P.V.; Golombek, D.A. The Times of Our Lives: Interaction Among Different Biological Periodicities. Front. Integr. Neurosci.; 2018; 12, 10. [DOI: https://dx.doi.org/10.3389/fnint.2018.00010]
86. Goldman, B.D. Mammalian photoperiodic system: Formal properties and neuroendocrine mechanisms of photoperiodic time measurement. J. Biol. Rhythm.; 2001; 16, pp. 283-301. [DOI: https://dx.doi.org/10.1177/074873001129001980]
87. Postolache, T.T.; Raheja, U.K. Body Rhythms/Biological Clocks. Encyclopedia of Mental Health; 2nd ed. Friedman, H.S. University of California: Berkeley, CA, USA, 2016; pp. 193-203.
88. Draper, C.F.; Duisters, K.; Weger, B.; Chakrabarti, A.; Harms, A.C.; Brennan, L.; Hankemeier, T.; Goulet, L.; Konz, T.; Martin, F.P. et al. Menstrual cycle rhythmicity: Metabolic patterns in healthy women. Sci. Rep.; 2018; 8, 14568. [DOI: https://dx.doi.org/10.1038/s41598-018-32647-0]
89. Baker, F.C.; Siboza, F.; Fuller, A. Temperature regulation in women: Effects of the menstrual cycle. Temperature; 2020; 7, pp. 226-262. [DOI: https://dx.doi.org/10.1080/23328940.2020.1735927]
90. de Mouzon, J.; Testart, J.; Lefevre, B.; Pouly, J.L.; Frydman, R. Time relationships between basal body temperature and ovulation or plasma progestins. Fertil. Steril.; 1984; 41, pp. 254-259. [DOI: https://dx.doi.org/10.1016/S0015-0282(16)47600-4]
91. Barton, M.; Wiesner, B.P. Thermogenic effect of progesterone. Lancet; 1945; 24, pp. 671-672. [DOI: https://dx.doi.org/10.1016/S0140-6736(45)91388-2] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/21004807]
92. Ekhart, D.; Wicht, H.; Kersken, T.; Ackermann, H.; Kaczmarczyk, M.; Pretzsch, G.; Alexander, H.; Korf, H.W. Dynamics of core body temperature cycles in long-term measurements under real life conditions in women. Chronobiol. Int.; 2018; 35, pp. 8-23. [DOI: https://dx.doi.org/10.1080/07420528.2017.1375942]
93. Refinetti, R. Circadian rhythmicity of body temperature and metabolism. Temperature; 2020; 7, pp. 321-362. [DOI: https://dx.doi.org/10.1080/23328940.2020.1743605]
94. Chidi-Ogbolu, N.; Baar, K. Effect of estrogen on musculoskeletal performance and injury risk. Front. Physiol.; 2019; 10, 1834. [DOI: https://dx.doi.org/10.3389/fphys.2018.01834]
95. Korf, H.W. Signaling pathways to and from the hypophysial pars tuberalis, an important center for the control of seasonal rhythms. Gen. Comp. Endocrinol.; 2018; 258, pp. 236-243. [DOI: https://dx.doi.org/10.1016/j.ygcen.2017.05.011]
96. Dopico, X.C.; Evangelou, M.; Ferreira, R.C.; Guo, H.; Pekalski, M.L.; Smyth, D.J.; Cooper, N.; Burren, O.S.; Fulford, A.J.; Hennig, B.J. et al. Widespread seasonal gene expression reveals annual differences in human immunity and physiology. Nat. Commun.; 2015; 6, 7000. [DOI: https://dx.doi.org/10.1038/ncomms8000] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/25965853]
97. Valdebenito, J.O.; Halimubieke, N.; Lendvai, Á.Z.; Figuerola, J.; Eichhorn, G.; Székely, T. Seasonal variation in sex-specific immunity in wild birds. Sci. Rep.; 2021; 11, pp. 1-11. [DOI: https://dx.doi.org/10.1038/s41598-020-80030-9] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/33446785]
98. Pierre, K.; Schlesinger, N.; Androulakis, I.P. The role of the hypothalamic-pituitary-adrenal axis in modulating seasonal changes in immunity. Physiol. Genom.; 2016; 48, pp. 719-738. [DOI: https://dx.doi.org/10.1152/physiolgenomics.00006.2016]
99. Khandaker, G.M.; Pearson, R.M.; Zammit, S.; Lewis, G.; Jones, P.B. Association of serum interleukin 6 and C-reactive protein in childhood with depression and psychosis in young adult life: A population-based longitudinal study. JAMA Psychiatry; 2014; 71, pp. 1121-1128. [DOI: https://dx.doi.org/10.1001/jamapsychiatry.2014.1332] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/25133871]
100. Wium-Andersen, M.K.; Ørsted, D.D.; Nielsen, S.F.; Nordestgaard, B.G. Elevated C-reactive protein levels, psychological distress, and depression in 73, 131 individuals. JAMA Psychiatry; 2013; 70, pp. 176-184. [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/23266538][DOI: https://dx.doi.org/10.1001/2013.jamapsychiatry.102]
101. Ferreira, R.C.; Freitag, D.F.; Cutler, A.J.; Howson, J.M.; Rainbow, D.B.; Smyth, D.J.; Kaptoge, S.; Clarke, P.; Boreham, C.; Coulson, R.M. et al. Functional IL6R 358Ala Allele Impairs Classical IL-6 Receptor Signaling and Influences Risk of Diverse Inflammatory Diseases. PLoS Genet.; 2013; 9, e1003444. [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/23593036][DOI: https://dx.doi.org/10.1371/journal.pgen.1003444]
102. Pell, J.P.; Cobbe, S.M. Seasonal variations in coronary heart disease. QJM; 1999; 92, pp. 689-696. [DOI: https://dx.doi.org/10.1093/qjmed/92.12.689]
103. Tsao, C.W.; Aday, A.W.; Almarzooq, Z.I.; Alonso, A.; Beaton, A.Z.; Bittencourt, M.S.; Boehme, A.K.; Buxton, A.E.; Carson, A.P.; Commodore-Mensah, Y. et al. Heart Disease and Stroke Statistics-2022 Update: A Report from the American Heart Association. Circulation; 2022; 145, pp. E153-E639. [DOI: https://dx.doi.org/10.1161/CIR.0000000000001052]
104. Nevo-Shor, A.; Kogan, S.; Joshua, B.Z.; Bahat-Dinur, A.; Novack, V.; Fraenkel, M. Seasonal changes in serum calcium, PTH and vitamin D levels in patients with primary hyperparathyroidism. Bone; 2016; 89, pp. 59-63. [DOI: https://dx.doi.org/10.1016/j.bone.2016.05.012]
105. Fyfe, T.; Dunnigan, M.G.; Hamilton, E.; Rae, R.J. Seasonal variation in serum lipids, and incidence and mortality of ischaemic heart disease. J. Atheroscler. Res.; 1968; 8, pp. 591-596. [DOI: https://dx.doi.org/10.1016/S0368-1319(68)80112-7]
106. Fuller, J.H.; Grainger, S.L.; Jarrett, R.J.; Keen, H. Possible seasonal variation of plasma lipids in a healthy population. Clin. Chim. Acta; 1974; 52, pp. 305-310. [DOI: https://dx.doi.org/10.1016/0009-8981(74)90115-6] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/4406772]
107. Woodhouse, P.R.; Khaw, K.T.; Plummer, M. Seasonal variation of serum lipids in an elderly population. Age Ageing; 1993; 22, pp. 273-278. [DOI: https://dx.doi.org/10.1093/ageing/22.4.273] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/8213333]
108. Manfredini, R.; Salmi, R.; Boari, B.; Manfredini, F. Seasonal Variation in Lipids: Should We Consider It More?. Am. J. Cardiol.; 2009; 104, pp. 739-740. [DOI: https://dx.doi.org/10.1016/j.amjcard.2009.04.017] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/19699358]
109. Donahoo, W.T.; Jensen, D.R.; Shepard, T.Y.; Eckel, R.H. Seasonal variation in lipoprotein lipase and plasma lipids in physically active, normal weight humans. J. Clin. Endocrinol. Metab.; 2000; 85, pp. 3065-3068. [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/10999787]
110. Cambras, T.; Baena-Fustegueras, J.A.; Pardina, E.; Ricart-Jané, D.; Rossell, J.; Díez-Noguera, A.; Peinado-Onsurbe, J. Seasonal variation in plasma lipids and lipases in young healthy humans. Chronobiol. Int.; 2017; 34, pp. 1248-1258. [DOI: https://dx.doi.org/10.1080/07420528.2017.1359618]
111. Ma, X.; Yan, H.; Zhang, H.; Wang, M.; Zhang, Q.; Zhou, X. Progress in the seasonal variations of blood lipids: A mini-review. Lipids Health Dis.; 2020; 19, 108. [DOI: https://dx.doi.org/10.1186/s12944-020-01237-3]
112. Bellastella, G.; Maiorino, M.I.; Scappaticcio, L.; De Bellis, A.; Mercadante, S.; Esposito, K.; Bellastella, A. Chronothyroidology: Chronobiological Aspects in Thyroid Function and Diseases. Life; 2021; 11, 426. [DOI: https://dx.doi.org/10.3390/life11050426]
113. Cargnelutti, F.; Pallotti, F.; Carlini, T.; Faja, F.; Vestri, A.R.; Fegatelli, D.A.; Lenzi, A.; Lombardo, F.; Paoli, D. A decade of WHO 2010: Total sperm number temporal trend and role of lifestyle factors. Asian J. Androl.; 2023; 10-4103. [DOI: https://dx.doi.org/10.4103/aja202297]
114. Thalange, N.K.; Gill, M.S.; Gill, L.; Whatmore, A.J.; Addison, G.M.; Price, D.A.; Clayton, P.E. Infradian rhythms in urinary growth hormone excretion. J. Clin. Endocrinol. Metab.; 1996; 81, pp. 100-106.
115. Gerasch, A.; Burchardt, U.; Klagge, M.; Balschun, D. Age dependency of infradian rhythms in enzymuria of female volunteers. Eur. J. Clin. Chem. Clin. Biochem.; 1997; 35, pp. 281-286. [DOI: https://dx.doi.org/10.1515/cclm.1997.35.4.281]
116. Burchardt, U.; Winkler, K.; Klagge, M.; Balschun, D.; Barth, A. Infradian biorhythms of enzymuria in man?. J. Clin. Chem. Clin. Biochem.; 1988; 26, pp. 491-496. [DOI: https://dx.doi.org/10.1515/cclm.1988.26.8.491] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/2906086]
117. Klingman, K.M.; Marsh, E.E.; Klerman, E.B.; Anderson, E.J.; Hall, J.E. Absence of circadian rhythms of gonadotropin secretion in women. J. Clin. Endocrinol. Metab.; 2011; 96, pp. 1456-1461. [DOI: https://dx.doi.org/10.1210/jc.2010-2739] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/21346063]
118. Rossmanith, W.G. Ultradian and circadian patterns in luteinizing hormone secretion during reproductive life in women. Hum. Reprod.; 1993; 8, (Suppl. S2), pp. 77-83. [DOI: https://dx.doi.org/10.1093/humrep/8.suppl_2.77] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/8276975]
119. Mortola, J.F.; Laughlin, G.A.; Yen, S.S.C. A circadian rhythm of serum follicle-stimulating hormone in women. J. Clin. Endocrinol. Metab.; 1992; 75, pp. 861-864. [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/1517378]
120. Kanabrocki, E.L.; Sothern, R.B.; Ryan, M.D.; Kahn, S.; Augustine, G.; Johnson, C.; Foley, S.; Gathing, A.; Eastman, G.; Friedman, N. et al. Circadian characteristics of serum calcium, magnesium and eight trace elements and of their metallo-moieties in urine of healthy middle-aged men. Clin. Ter.; 2008; 159, pp. 329-346.
121. Micozkadioglu, H.; Ozelsancak, R.; Yildiz, I.; Erken, E.; Zumrutdal, A.; Torun, D.; Haberal, M. Circadian rhythm of serum phosphate, calcium and parathyroid hormone levels in hemodialysis patients. Clin. Lab.; 2013; 59, pp. 79-84. [DOI: https://dx.doi.org/10.7754/Clin.Lab.2012.120205]
122. Choi, J.H.; Lee, B.; Lee, J.Y.; Kim, C.H.; Park, B.; Kim, D.Y.; Kim, H.J.; Park, D.Y. Relationship between Sleep Duration, Sun Exposure, and Serum 25-Hydroxyvitamin D Status: A Cross-sectional Study. Sci. Rep.; 2020; 10, 4168. [DOI: https://dx.doi.org/10.1038/s41598-020-61061-8]
123. Masood, T.; Kushwaha, R.S.; Singh, R.; Sailwal, S.; Pandey, H.; Varma, A.; Singh, R.K.; Cornelissen, G. Circadian rhythm of serum 25 (OH) vitamin D, calcium and phosphorus levels in the treatment and management of type-2 diabetic patients. Drug Discov. Ther.; 2015; 9, pp. 70-74. [DOI: https://dx.doi.org/10.5582/ddt.2015.01002]
124. Zhu, B.; Dacso, C.C.; O’Malley, B.W. Unveiling “Musica Universalis” of the Cell: A Brief History of Biological 12-Hour Rhythms. J. Endocr. Soc.; 2018; 2, pp. 727-752. [DOI: https://dx.doi.org/10.1210/js.2018-00113]
125. James, S.M.; Honn, K.A.; Gaddameedhi, S.; Van Dongen, H.P.A. Shift Work: Disrupted Circadian Rhythms and Sleep—Implications for Health and Well-Being. Curr. Sleep Med. Rep.; 2017; 3, pp. 104-112. [DOI: https://dx.doi.org/10.1007/s40675-017-0071-6]
126. Vosko, A.M.; Colwell, C.S.; Avidan, A.Y. Jet lag syndrome: Circadian organization, pathophysiology, and management strategies. Nat. Sci. Sleep; 2010; 2, pp. 187-198. [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/23616709]
127. Caliandro, R.; Streng, A.A.; van Kerkhof, L.W.M.; van der Horst, G.T.J.; Chaves, I. Social Jetlag and Related Risks for Human Health: A Timely Review. Nutrients; 2021; 13, 4543. [DOI: https://dx.doi.org/10.3390/nu13124543]
128. Hebl, J.T.; Velasco, J.; McHill, A.W. Work Around the Clock: How Work Hours Induce Social Jetlag and Sleep Deficiency. Clin. Chest Med.; 2022; 43, pp. 249-259. [DOI: https://dx.doi.org/10.1016/j.ccm.2022.02.003]
129. Blume, C.; Garbazza, C.; Spitschan, M. Effects of light on human circadian rhythms, sleep and mood. Somnologie; 2019; 23, 147. [DOI: https://dx.doi.org/10.1007/s11818-019-00215-x]
130. Maury, E. Off the Clock: From Circadian Disruption to Metabolic Disease. Int. J. Mol. Sci.; 2019; 20, 1597. [DOI: https://dx.doi.org/10.3390/ijms20071597]
131. Pan, A.; Schernhammer, E.S.; Sun, Q.; Hu, F.B. Rotating Night Shift Work and Risk of Type 2 Diabetes: Two Prospective Cohort Studies in Women. PLoS Med.; 2011; 8, e1001141. [DOI: https://dx.doi.org/10.1371/journal.pmed.1001141]
132. Haupt, C.M.; Alte, D.; Dörr, M.; Robinson, D.M.; Felix, S.B.; John, U.; Völzke, H. The relation of exposure to shift work with atherosclerosis and myocardial infarction in a general population. Atherosclerosis; 2008; 201, pp. 205-211. [DOI: https://dx.doi.org/10.1016/j.atherosclerosis.2007.12.059]
133. Xiang, K.; Xu, Z.; Hu, Y.Q.; He, Y.S.; Wu, G.C.; Li, T.Y.; Wang, X.R.; Ding, L.H.; Zhang, Q.; Tao, S.S. et al. Circadian clock genes as promising therapeutic targets for autoimmune diseases. Autoimmun. Rev.; 2021; 20, 102866. [DOI: https://dx.doi.org/10.1016/j.autrev.2021.102866]
134. Li, Y.; Ma, J.; Yao, K.; Su, W.; Tan, B.; Wu, X.; Huang, X.; Li, T.; Yin, Y.; Tosini, G. et al. Circadian rhythms and obesity: Timekeeping governs lipid metabolism. J. Pineal Res.; 2020; 69, e12682. [DOI: https://dx.doi.org/10.1111/jpi.12682]
135. Shafi, A.A.; Knudsen, K.E. Cancer and the Circadian Clock. Cancer Res.; 2019; 79, pp. 3806-3814. [DOI: https://dx.doi.org/10.1158/0008-5472.CAN-19-0566]
136. Kamdar, B.B.; Needham, D.M.; Collop, N.A. Sleep Deprivation in Critical Illness: Its Role in Physical and Psychological Recovery. J. Intensive Care Med.; 2012; 27, pp. 97-111. [DOI: https://dx.doi.org/10.1177/0885066610394322] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/21220271]
137. Mingrone, G.; Granato, L.; Valera-Mora, E.; Iaconelli, A.; Calvani, M.F.; Bracaglia, R.; Manco, M.; Nanni, G.; Castagneto, M. Ultradian ghrelin pulsatility is disrupted in morbidly obese subjects after weight loss induced by malabsorptive bariatric surgery. Am. J. Clin. Nutr.; 2006; 83, pp. 1017-1024. [DOI: https://dx.doi.org/10.1093/ajcn/83.5.1017] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/16685041]
138. Sarabdjitsingh, R.A.; Spiga, F.; Oitzl, M.S.; Kershaw, Y.; Meijer, O.C.; Lightman, S.L.; De Kloet, E.R. Recovery from Disrupted Ultradian Glucocorticoid Rhythmicity Reveals a Dissociation Between Hormonal and Behavioural Stress Responsiveness. J. Neuroendocr.; 2010; 22, pp. 862-871.
139. Lieberman, J.L.; De Souza, M.J.; Wagstaff, D.A.; Williams, N.I. Menstrual Disruption with Exercise is not Linked to an Energy Availability Threshold. Med. Sci. Sport. Exerc.; 2018; 50, pp. 551-561. [DOI: https://dx.doi.org/10.1249/MSS.0000000000001451] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/29023359]
140. Monje, F.J.; Cicvaric, A.; Acevedo Aguilar, J.P.; Elbau, I.; Horvath, O.; Diao, W.; Glat, M.; Pollak, D.D. Disrupted ultradian activity rhythms and differential expression of several clock genes in interleukin-6-deficient mice. Front. Neurol.; 2017; 8, 99. [DOI: https://dx.doi.org/10.3389/fneur.2017.00099] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/28382017]
141. Fraser, C.G. Biological Variation: From Principles to Practice; Amer. Assoc. for Clinical Chemistry: Washington, DC, USA, 2001.
142. Fraser, C.G. Age-Related Changes in Laboratory Test Results. Drugs Aging; 1993; 3, pp. 246-257. [DOI: https://dx.doi.org/10.2165/00002512-199303030-00006] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/8324300]
143. EFLM Biological Variation. Available online: https://biologicalvariation.eu/ (accessed on 24 February 2022).
144. Coskun, A.; Sandberg, S.; Unsal, I.; Yavuz, F.G.; Cavusoglu, C.; Serteser, M.; Kilercik, M.; Aarsand, A.K. Personalized reference intervals–statistical approaches and considerations. Clin. Chem. Lab. Med.; 2021; 60, pp. 629-635. [DOI: https://dx.doi.org/10.1515/cclm-2021-1066]
145. Coskun, A.; Sandberg, S.; Unsal, I.; Cavusoglu, C.; Serteser, M.; Kilercik, M.; Aarsand, A.K. Personalized reference intervals: Using estimates of within-subject or within-person biological variation requires different statistical approaches. Clin. Chim. Acta; 2022; 524, pp. 201-202. [DOI: https://dx.doi.org/10.1016/j.cca.2021.10.034]
146. Coskun, A.; Sandberg, S.; Unsal, I.; Serteser, M.; Aarsand, A.K. Personalized reference intervals: From theory to practice. Crit. Rev. Clin. Lab. Sci.; 2022; 59, pp. 501-516. [DOI: https://dx.doi.org/10.1080/10408363.2022.2070905]
147. Coşkun, A.; Sandberg, S.; Unsal, I.; Cavusoglu, C.; Serteser, M.; Kilercik, M.; Aarsand, A.K. Personalized Reference Intervals in Laboratory Medicine: A New Model Based on Within-Subject Biological Variation. Clin. Chem.; 2021; 67, pp. 374-384. [DOI: https://dx.doi.org/10.1093/clinchem/hvaa233]
148. Carobene, A.; Graziani, M.S.; Cascio, C.L.; Tretti, L.; Cremonese, E.; Yabarek, T.; Gambaro, G.; Ceriotti, F. Age dependence of within-subject biological variation of nine common clinical chemistry analytes. Clin. Chem. Lab. Med.; 2012; 50, pp. 841-844. [DOI: https://dx.doi.org/10.1515/cclm-2011-0868] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/22628327]
149. Jones, G.R.D. Estimates of Within-Subject Biological Variation Derived from Pathology Databases: An Approach to Allow Assessment of the Effects of Age, Sex, Time between Sample Collections, and Analyte Concentration on Reference Change Values. Clin. Chem.; 2019; 65, pp. 579-588. [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/30692116][DOI: https://dx.doi.org/10.1373/clinchem.2018.290841]
150. Ricós, C.; Iglesias, N.; García-Lario, J.V.; Simón, M.; Cava, F.; Hernández, A.; Perich, C.; Minchinela, J.; Alvarez, V.; Doménech, M.V. et al. Within-subject biological variation in disease: Collated data and clinical consequences. Ann. Clin. Biochem.; 2007; 44, pp. 343-352. [DOI: https://dx.doi.org/10.1258/000456307780945633] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/17594781]
151. Ricós, C.; Álvarez, V.; Perich, C.; Fernández-Calle, P.; Minchinela, J.; Cava, F.; Biosca, C.; Boned, B.; Doménech, M.; García-Lario, J.V. et al. Rationale for using data on biological variation. Clin. Chem. Lab. Med.; 2015; 53, pp. 863-870. [DOI: https://dx.doi.org/10.1515/cclm-2014-1142] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/25928757]
152. Coşkun, A.; Carobene, A.; Kilercik, M.; Serteser, M.; Sandberg, S.; Aarsand, A.K.; Fernandez-Calle, P.; Jonker, N.; Bartlett, W.A.; Díaz-Garzón, J. et al. Within-subject and between-subject biological variation estimates of 21 hematological parameters in 30 healthy subjects. Clin. Chem. Lab. Med.; 2018; 56, pp. 1309-1318. [DOI: https://dx.doi.org/10.1515/cclm-2017-1155]
153. Carobene, A. Reliability of biological variation data available in an online database: Need for improvement. Clin. Chem. Lab. Med.; 2015; 53, pp. 871-877. [DOI: https://dx.doi.org/10.1515/cclm-2014-1133]
154. Carobene, A.; Braga, F.; Roraas, T.; Sandberg, S.; Bartlett, W.A. A systematic review of data on biological variation for alanine aminotransferase, aspartate aminotransferase and γ-glutamyl transferase. Clin. Chem. Lab. Med.; 2013; 51, pp. 1997-2007. [DOI: https://dx.doi.org/10.1515/cclm-2013-0096]
155. Carobene, A.; Strollo, M.; Jonker, N.; Barla, G.; Bartlett, W.A.; Sandberg, S.; Sylte, M.S.; Røraas, T.; Sølvik, U.Ø.; Fernandez-Calle, P. et al. Sample collections from healthy volunteers for biological variation estimates’ update: A new project undertaken by the Working Group on Biological Variation established by the European Federation of Clinical Chemistry and Laboratory Medicine. Clin. Chem. Lab. Med.; 2016; 54, pp. 1599-1608. [DOI: https://dx.doi.org/10.1515/cclm-2016-0035]
156. Carobene, A.; Aarsand, A.K.; Bartlett, W.A.; Coskun, A.; Diaz-Garzon, J.; Fernandez-Calle, P.; Guerra, E.; Jonker, N.; Locatelli, M.; Plebani, M. et al. The European Biological Variation Study (EuBIVAS): A summary report. Clin. Chem. Lab. Med.; 2022; 60, pp. 505-517. [DOI: https://dx.doi.org/10.1515/cclm-2021-0370]
157. Aarsand, A.K.; Røraas, T.; Fernandez-Calle, P.; Ricos, C.; Díaz-Garzón, J.; Jonker, N.; Perich, C.; González-Lao, E.; Carobene, A.; Minchinela, J. et al. The Biological Variation Data Critical Appraisal Checklist: A Standard for Evaluating Studies on Biological Variation. Clin. Chem.; 2018; 64, pp. 501-514. [DOI: https://dx.doi.org/10.1373/clinchem.2017.281808]
158. Desirable Biological Variation Database Specifications-Westgard. Available online: https://www.westgard.com/biodatabase1.htm (accessed on 24 February 2022).
159. Perich, C.; Minchinela, J.; Ricós, C.; Fernández-Calle, P.; Alvarez, V.; Doménech, M.V.; Simón, M.; Biosca, C.; Boned, B.; García-Lario, J.V. et al. Biological variation database: Structure and criteria used for generation and update. Clin. Chem. Lab. Med.; 2015; 53, pp. 299-305. [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/25415636][DOI: https://dx.doi.org/10.1515/cclm-2014-0739]
160. Şahin, T.; Tozcu, D. Circadian rhythm and obesity. Int. J. Sci. Lett.; 2022; 4, pp. 202-219. [DOI: https://dx.doi.org/10.38058/ijsl.1053393]
161. Parrinello, C.M.; Lutsey, P.L.; Couper, D.; Eckfeldt, J.H.; Steffes, M.W.; Coresh, J.; Selvin, E. Total Short-term Variability in Biomarkers of Hyperglycemia in Older Adults. Clin. Chem.; 2015; 61, pp. 1540-1541. [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/26503966][DOI: https://dx.doi.org/10.1373/clinchem.2015.246231]
162. Ozarda, Y.; Sikaris, K.; Streichert, T.; Macri, J. Distinguishing reference intervals and clinical decision limits-A review by the IFCC Committee on Reference Intervals and Decision Limits. Crit. Rev. Clin. Lab. Sci.; 2018; 55, pp. 420-431. [DOI: https://dx.doi.org/10.1080/10408363.2018.1482256] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/30047297]
163. Horowitz, G.L.; Altaie, S.; Boyd, J.C.; Ceriotti, F.; Garg, U.; Horn, P. EP28-A3c Defining, Establishing, and Verifying Reference Intervals in the Clinical Laboratory; Approved Guideline; 3rd ed. Clinical And Laboratory Standards İnstitute: Wayne, PA, USA, 2008.
164. Pelsers, M.M.; Chapelle, J.P.; Knapen, M.; Vermeer, C.; Muijtjens, A.M.; Hermens, W.T.; Glatz, J.F. Influence of age and sex and day-to-day and within-day biological variation on plasma concentrations of fatty acid-binding protein and myoglobin in healthy subjects. Clin. Chem.; 1999; 45, pp. 441-443. [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/10053065]
165. Hilderink, J.M.; Klinkenberg, L.J.; Aakre, K.M.; de Wit, N.C.; Henskens, Y.M.; van der Linden, N.; Bekers, O.; Rennenberg, R.J.; Koopmans, R.P.; Meex, S.J. Within-day biological variation and hour-to-hour reference change values for hematological parameters. Clin. Chem. Lab. Med.; 2017; 55, pp. 1013-1024. [DOI: https://dx.doi.org/10.1515/cclm-2016-0716]
166. Madsen, A.T.; Hojbjerg, J.A.; Sorensen, B.S.; Winther-Larsen, A. Day-to-day and within-day biological variation of cell-free DNA. EBioMedicine; 2019; 49, pp. 284-290. [DOI: https://dx.doi.org/10.1016/j.ebiom.2019.10.008]
167. Bailey, D.; Bevilacqua, V.; Colantonio, D.A.; Pasic, M.D.; Perumal, N.; Chan, M.K.; Adeli, K. Pediatric within-day biological variation and quality specifications for 38 biochemical markers in the CALIPER cohort. Clin. Chem.; 2014; 60, pp. 518-529. [DOI: https://dx.doi.org/10.1373/clinchem.2013.214312]
168. Baskin, L.; Chin, A.; Abdullah, A.; Naugler, C. Errors in patient preparation, specimen collection, anticoagulant and preservative use: How to avoid such pre-analytical errors. Accurate Results in the Clinical Laboratory; 2nd ed. Dasgupta, A.; Sepulveda, J.L. Elsevier: Amsterdam, The Netherlands, 2019; pp. 11-26.
169. Schüring, A.N.; Kelsch, R.; Pierściński, G.; Nofer, J.-R. Brief Communication Clinical Chemistry Establishing Reference Intervals for Sex Hormones on the Analytical Platforms Advia Centaur and Immulite 2000XP. Ann. Lab. Med.; 2016; 36, pp. 55-59.
170. Verdonk, S.J.E.; Vesper, H.W.; Martens, F.; Sluss, P.M.; Hillebrand, J.J.; Heijboer, A.C. Estradiol reference intervals in women during the menstrual cycle, postmenopausal women and men using an LC-MS/MS method. Clin. Chim. Acta; 2019; 495, pp. 198-204. [DOI: https://dx.doi.org/10.1016/j.cca.2019.04.062]
171. Stricker, R.; Eberhart, R.; Chevailler, M.C.; Quinn, F.A.; Bischof, P.; Stricker, R. Establishment of detailed reference values for luteinizing hormone, follicle stimulating hormone, estradiol, and progesterone during different phases of the menstrual cycle on the Abbott ARCHITECT analyzer. Clin. Chem. Lab. Med.; 2006; 44, pp. 883-887. [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/16776638][DOI: https://dx.doi.org/10.1515/CCLM.2006.160]
172. Fraser, C.G. Reference change values: The way forward in monitoring. Ann. Clin. Biochem.; 2009; 46, pp. 264-265. [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/19264828][DOI: https://dx.doi.org/10.1258/acb.2009.009006]
173. Dittadi, R.; Fabricio, A.S.C.; Gion, M. Biological variation and reference change value as decision criteria in clinical use of tumor biomarkers. Are they really useful?. Clin. Chem. Lab. Med.; 2022; 60, pp. e136-e137. [DOI: https://dx.doi.org/10.1515/cclm-2022-0047]
174. Mazri, F.H.; Manaf, Z.A.; Shahar, S.; Ludin, A.F.M. The Association between Chronotype and Dietary Pattern among Adults: A Scoping Review. Int. J. Environ. Res. Public Health; 2019; 17, 68. [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/31861810][DOI: https://dx.doi.org/10.3390/ijerph17010068]
175. Kalmbach, D.A.; Schneider, L.D.; Cheung, J.; Bertrand, S.J.; Kariharan, T.; Pack, A.I.; Gehrman, P.R. Genetic Basis of Chronotype in Humans: Insights From Three Landmark GWAS. Sleep; 2017; 40, zsw048. [DOI: https://dx.doi.org/10.1093/sleep/zsw048] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/28364486]
176. Montaruli, A.; Castelli, L.; Mulè, A.; Scurati, R.; Esposito, F.; Galasso, L.; Roveda, E. Biological Rhythm and Chronotype: New Perspectives in Health. Biomolecules; 2021; 11, 487. [DOI: https://dx.doi.org/10.3390/biom11040487] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/33804974]
177. Sempere-Rubio, N.; Aguas, M.; Faubel, R. Association between Chronotype, Physical Activity and Sedentary Behaviour: A Systematic Review. Int. J. Environ. Res. Public Health; 2022; 19, 9646. [DOI: https://dx.doi.org/10.3390/ijerph19159646] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/35955020]
178. Chauhan, S.; Norbury, R.; Faßbender, K.C.; Ettinger, U.; Kumari, V. Beyond Sleep: A Multidimensional Model of Chronotype. Neurosci. Biobehav. Rev.; 2023; 148, 105114. [DOI: https://dx.doi.org/10.1016/j.neubiorev.2023.105114]
179. Horne, J.A.; Östberg, O. Individual differences in human circadian rhythms. Biol. Psychol.; 1977; 5, pp. 179-190. [DOI: https://dx.doi.org/10.1016/0301-0511(77)90001-1] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/922076]
180. van der Merwe, C.; Münch, M.; Kruger, R. Chronotype Differences in Body Composition, Dietary Intake and Eating Behavior Outcomes: A Scoping Systematic Review. Adv. Nutr.; 2022; 13, pp. 2357-2405. [DOI: https://dx.doi.org/10.1093/advances/nmac093]
181. Baron, K.G.; Reid, K.J.; Kern, A.S.; Zee, P.C. Role of sleep timing in caloric intake and BMI. Obesity; 2011; 19, pp. 1374-1381. [DOI: https://dx.doi.org/10.1038/oby.2011.100] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/21527892]
182. Merikanto, I.; Lahti, T.; Puolijoki, H.; Vanhala, M.; Peltonen, M.; Laatikainen, T.; Vartiainen, E.; Salomaa, V.; Kronholm, E.; Partonen, T. Associations of chronotype and sleep with cardiovascular diseases and type 2 diabetes. Chronobiol. Int.; 2013; 30, pp. 470-477. [DOI: https://dx.doi.org/10.3109/07420528.2012.741171]
183. Merikanto, I.; Lahti, T.; Kronholm, E.; Peltonen, M.; Laatikainen, T.; Vartiainen, E.; Salomaa, V.; Partonen, T. Evening types are prone to depression. Chronobiol. Int.; 2013; 30, pp. 719-725. [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/23688117][DOI: https://dx.doi.org/10.3109/07420528.2013.784770]
184. Morera-Fumero, A.L. Chronotype as modulator of morning serum melatonin levels. Actas Españolas Psiquiatr.; 2013; 41, pp. 149-153.
185. Adan, A.; Archer, S.N.; Hidalgo, M.P.; Milia LDi Natale, V.; Randler, C. Circadian Typology: A Comprehensive Review. Chronobiol. Int.; 2012; 29, pp. 1153-1175. [DOI: https://dx.doi.org/10.3109/07420528.2012.719971] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/23004349]
186. Vera, B.; Dashti, H.S.; Gómez-Abellán, P.; Hernández-Martínez, A.M.; Esteban, A.; Scheer, F.A.; Saxena, R.; Garaulet, M. Modifiable lifestyle behaviors, but not a genetic risk score, associate with metabolic syndrome in evening chronotypes. Sci. Rep.; 2018; 8, pp. 1-11. [DOI: https://dx.doi.org/10.1038/s41598-017-18268-z]
187. Lucassen, E.A.; Zhao, X.; Rother, K.I.; Mattingly, M.S.; Courville, A.B.; De Jonge, L.; Csako, G.; Cizza, G. Sleep Extension Study Group. Evening Chronotype Is Associated with Changes in Eating Behavior, More Sleep Apnea, and Increased Stress Hormones in Short Sleeping Obese Individuals. PLoS ONE; 2013; 8, e56519. [DOI: https://dx.doi.org/10.1371/journal.pone.0056519]
188. Mormont, M.C.; Waterhouse, J. Contribution of the rest–activity circadian rhythm to quality of life in cancer patients. Chronobiol. Int.; 2002; 19, pp. 313-323. [DOI: https://dx.doi.org/10.1081/CBI-120002606]
189. Cohen, L.; Cole, S.W.; Sood, A.K.; Prinsloo, S.; Kirschbaum, C.; Arevalo, J.M.; Jennings, N.B.; Scott, S.; Vence, L.; Wei, Q. et al. Depressive Symptoms and Cortisol Rhythmicity Predict Survival in Patients with Renal Cell Carcinoma: Role of Inflammatory Signaling. PLoS ONE; 2012; 7, e42324. [DOI: https://dx.doi.org/10.1371/journal.pone.0042324]
190. Sephton, S.E.; Lush, E.; Dedert, E.A.; Floyd, A.R.; Rebholz, W.N.; Dhabhar, F.S.; Spiegel, D.; Salmon, P. Diurnal cortisol rhythm as a predictor of lung cancer survival. Brain Behav. Immun.; 2013; 30, pp. S163-S170. [DOI: https://dx.doi.org/10.1016/j.bbi.2012.07.019] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/22884416]
191. Lévi, F.; Dugué, P.A.; Innominato, P.; Karaboué, A.; Dispersyn, G.; Parganiha, A.; Giacchetti, S.; Moreau, T.; Focan, C.; Waterhouse, J. et al. Wrist actimetry circadian rhythm as a robust predictor of colorectal cancer patients survival. Chronobiol. Int.; 2014; 31, pp. 891-900. [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/24927369][DOI: https://dx.doi.org/10.3109/07420528.2014.924523]
192. Sephton, S.E.; Sapolsky, R.M.; Kraemer, H.C.; Spiegel, D. Diurnal cortisol rhythm as a predictor of breast cancer survival. J. Natl. Cancer Inst.; 2000; 92, pp. 994-1000. [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/10861311][DOI: https://dx.doi.org/10.1093/jnci/92.12.994]
193. Galasso, L.; Montaruli, A.; Mulè, A.; Castelli, L.; Bruno, E.; Pasanisi, P.; Caumo, A.; Esposito, F.; Roveda, E. Rest-activity rhythm in breast cancer survivors: An update based on non-parametric indices. Chronobiol. Int.; 2020; 37, pp. 946-951. [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/32326769][DOI: https://dx.doi.org/10.1080/07420528.2020.1756839]
194. Allison, K.C.; Goel, N.; Ahima, R.S. Delayed Timing of Eating: Impact on Weight and Metabolism. Curr. Obes. Rep.; 2014; 3, pp. 91-100. [DOI: https://dx.doi.org/10.1007/s13679-013-0084-5] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/26626470]
195. Spiegel, K.; Tasali, E.; Leproult, R.; Van Cauter, E. Effects of poor and short sleep on glucose metabolism and obesity risk. Nat. Rev. Endocrinol.; 2009; 5, pp. 253-561. [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/19444258][DOI: https://dx.doi.org/10.1038/nrendo.2009.23]
196. Spiegel, K.; Tasali, E.; Penev, P.; Van Cauter, E. Brief communication: Sleep curtailment in healthy young men is associated with decreased leptin levels, elevated ghrelin levels, and increased hunger and appetite. Ann. Intern. Med.; 2004; 141, pp. 846-850. [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/15583226][DOI: https://dx.doi.org/10.7326/0003-4819-141-11-200412070-00008]
197. Taheri, S.; Lin, L.; Austin, D.; Young, T.; Mignot, E. Short sleep duration is associated with reduced leptin, elevated ghrelin, and increased body mass index. PLoS Med.; 2004; 1, pp. 210-217. [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/15602591][DOI: https://dx.doi.org/10.1371/journal.pmed.0010062]
198. Amiama-Roig, A.; Verdugo-Sivianes, E.M.; Carnero, A.; Blanco, J.R. Chronotherapy: Circadian Rhythms and Their Influence in Cancer Therapy. Cancers; 2022; 14, 5071. [DOI: https://dx.doi.org/10.3390/cancers14205071] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/36291855]
199. Greco, C.M.; Sassone-Corsi, P. Personalized medicine and circadian rhythms: Opportunities for modern society. J. Exp. Med.; 2020; 217, e20200702. [DOI: https://dx.doi.org/10.1084/jem.20200702]
200. Chang, A.M.; Aeschbach, D.; Duffy, J.F.; Czeisler, C.A. Evening use of light-emitting eReaders negatively affects sleep, circadian timing, and next-morning alertness. Proc. Natl. Acad. Sci. USA; 2015; 112, pp. 1232-1237. [DOI: https://dx.doi.org/10.1073/pnas.1418490112] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/25535358]
201. Koike, N.; Yoo, S.H.; Huang, H.C.; Kumar, V.; Lee, C.; Kim, T.K.; Takahashi, J.S. Transcriptional architecture and chromatin landscape of the core circadian clock in mammals. Science; 2012; 338, pp. 349-354. [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/22936566][DOI: https://dx.doi.org/10.1126/science.1226339]
202. Mure, L.S.; Le, H.D.; Benegiamo, G.; Chang, M.W.; Rios, L.; Jillani, N.; Ngotho, M.; Kariuki, T.; Dkhissi-Benyahya, O.; Cooper, H.M. et al. Diurnal transcriptome atlas of a primate across major neural and peripheral tissues. Science; 2018; 359, eaao0318. [DOI: https://dx.doi.org/10.1126/science.aao0318] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/29439024]
203. Rana, S.; Prabhu, S.D.; Young, M.E. Chronobiological Influence Over Cardiovascular Function: The Good, the Bad, and the Ugly. Circ. Res.; 2020; 126, pp. 258-279. [DOI: https://dx.doi.org/10.1161/CIRCRESAHA.119.313349] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/31944922]
204. Ezagouri, S.; Zwighaft, Z.; Sobel, J.; Baillieul, S.; Doutreleau, S.; Ladeuix, B.; Golik, M.; Verges, S.; Asher, G. Physiological and Molecular Dissection of Daily Variance in Exercise Capacity. Cell Metab.; 2019; 30, pp. 78-91. [DOI: https://dx.doi.org/10.1016/j.cmet.2019.03.012] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/31006590]
205. Asher, G.; Sassone-Corsi, P. Time for food: The intimate interplay between nutrition, metabolism, and the circadian clock. Cell; 2015; 161, pp. 84-92. [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/25815987][DOI: https://dx.doi.org/10.1016/j.cell.2015.03.015]
206. Guan, D.; Xiong, Y.; Borck, P.C.; Jang, C.; Doulias, P.T.; Papazyan, R.; Fang, B.; Jiang, C.; Zhang, Y.; Briggs, E.R. et al. Diet-Induced Circadian Enhancer Remodeling Synchronizes Opposing Hepatic Lipid Metabolic Processes. Cell; 2018; 174, pp. 831-842. [DOI: https://dx.doi.org/10.1016/j.cell.2018.06.031]
207. Qin, X.; Li, Q.; Chen, W.; Bai, Y.; Baban, B.; Mao, J. The circadian expression of osteogenic factors in periodontal tissue loading mechanical force: New concepts of the personalized orthodontic care. EPMA J.; 2019; 10, pp. 13-20. [DOI: https://dx.doi.org/10.1007/s13167-019-0161-2] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/30984310]
208. Golubnitschaja, O. Feeling cold and other underestimated symptoms in breast cancer: Anecdotes or individual profiles for advanced patient stratification?. EPMA J.; 2017; 8, pp. 17-22. [DOI: https://dx.doi.org/10.1007/s13167-017-0086-6]
209. Masri, S.; Sassone-Corsi, P. The emerging link between cancer, metabolism, and circadian rhythms. Nat. Med.; 2018; 24, pp. 1795-1803. [DOI: https://dx.doi.org/10.1038/s41591-018-0271-8]
210. Acker, J.; Golubnitschaja, O.; Büttner-Teleaga, A.; Richter, K. Wrist actigraphic approach in primary, secondary and tertiary care based on the principles of predictive, preventive and personalised (3P) medicine. EPMA J.; 2021; 12, pp. 349-363. [DOI: https://dx.doi.org/10.1007/s13167-021-00250-5] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/34377218]
211. Pearson, J.A.; Wong, F.S.; Wen, L. Crosstalk between circadian rhythms and the microbiota. Immunology; 2020; 161, pp. 278-290. [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/33090484][DOI: https://dx.doi.org/10.1111/imm.13278]
212. Leng, Y.; Musiek, E.S.; Hu, K.; Cappuccio, F.P.; Yaffe, K. Association between circadian rhythms and neurodegenerative diseases. Lancet Neurol.; 2019; 18, pp. 307-318. [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/30784558][DOI: https://dx.doi.org/10.1016/S1474-4422(18)30461-7]
213. Portaluppi, F.; Tiseo, R.; Smolensky, M.H.; Hermida, R.C.; Ayala, D.E.; Fabbian, F. Circadian rhythms and cardiovascular health. Sleep Med. Rev.; 2012; 16, pp. 151-166. [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/21641838][DOI: https://dx.doi.org/10.1016/j.smrv.2011.04.003]
214. Griffis, C.G.; Mistry, J.; Islam, K.; Cutler, T.; Colwell, C.S.; Garfinkel, A. Circadian and ultradian rhythms in normal mice and in a mouse model of Huntington’s disease. Chronobiol. Int.; 2022; 39, pp. 513-524. [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/34983274][DOI: https://dx.doi.org/10.1080/07420528.2021.2014516]
215. Hartsock, M.J.; Strnad, H.K.; Spencer, R.L. Iterative Metaplasticity Across Timescales: How Circadian, Ultradian, and Infradian Rhythms Modulate Memory Mechanisms. J. Biol. Rhythm.; 2022; 37, pp. 29-42. [DOI: https://dx.doi.org/10.1177/07487304211058256] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/34781753]
216. Olovnikov, A. Lunasensor, infradian rhythms, telomeres, and the chronomere program of aging. Ann. N. Y. Acad. Sci.; 2005; 1057, pp. 112-132. [DOI: https://dx.doi.org/10.1196/annals.1356.006] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/16399890]
217. Turbill, C.; Ruf, T.; Smith, S.; Bieber, C. Seasonal variation in telomere length of a hibernating rodent. Biol. Lett.; 2013; 9, 20121095. [DOI: https://dx.doi.org/10.1098/rsbl.2012.1095] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/23389666]
218. Rehkopf, D.H.; Dow, W.H.; Rosero-Bixby, L.; Lin, J.; Epel, E.S.; Blackburn, E.H. Seasonal variation of peripheral blood leukocyte telomere length in Costa Rica: A population-based observational study. Am. J. Hum. Biol.; 2014; 26, pp. 367-375. [DOI: https://dx.doi.org/10.1002/ajhb.22529]
219. Beaulieu, M.; Benoit, L.; Abaga, S.; Kappeler, P.M.; Charpentier, M.J.E. Mind the cell: Seasonal variation in telomere length mirrors changes in leucocyte profile. Mol. Ecol.; 2017; 26, pp. 5603-5613. [DOI: https://dx.doi.org/10.1111/mec.14329]
220. Ledda, C.; Loreto, C.; Rapisarda, V. Telomere Length as a Biomarker of Biological Aging in Shift Workers. Appl. Sci.; 2020; 10, 2764. [DOI: https://dx.doi.org/10.3390/app10082764]
221. Daios, S.; Anogeianaki, A.; Kaiafa, G.; Kontana, A.; Veneti, S.; Gogou, C.; Karlafti, E.; Pilalas, D.; Kanellos, I.; Savopoulos, C. Telomere Length as a Marker of Biological Aging: A Critical Review of Recent Literature. Curr. Med. Chem.; 2022; 29, pp. 5478-5495. [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/35838223]
222. Bittman, E.L. Timing in the Testis. J. Biol. Rhythm.; 2016; 31, pp. 12-36. [DOI: https://dx.doi.org/10.1177/0748730415618297] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/26656623]
223. Mao, H.; Feng, L.; Yang, W.X. Environmental factors contributed to circannual rhythm of semen quality. Chronobiol. Int.; 2017; 34, pp. 411-425. [DOI: https://dx.doi.org/10.1080/07420528.2017.1280046] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/28128993]
224. Xue, T.; Song, C.; Wang, Q.; Wang, Y.; Chen, G. Investigations of the CLOCK and BMAL1 Proteins Binding to DNA: A Molecular Dynamics Simulation Study. PLoS ONE; 2016; 11, e0155105. [DOI: https://dx.doi.org/10.1371/journal.pone.0155105] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/27153104]
225. González-Arto, M.; Vicente-Carrillo, A.; Martínez-Pastor, F.; Fernández-Alegre, E.; Roca, J.; Miró, J.; Rigau, T.; Rodríguez-Gil, J.E.; Pérez-Pé, R.; Muiño-Blanco, T. et al. Melatonin receptors MT1 and MT2 are expressed in spermatozoa from several seasonal and nonseasonal breeder species. Theriogenology; 2016; 86, pp. 1958-1968. [DOI: https://dx.doi.org/10.1016/j.theriogenology.2016.06.016]
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; Zarrabi, Ali 2
1 Department of Medical Biochemistry, School of Medicine, Acibadem Mehmet Ali Aydinlar University, Ataşehir, 34752 Istanbul, Turkey; Acibadem Labmed Clinical Laboratories, Ataşehir, 34752 Istanbul, Turkey
2 Department of Biomedical Engineering, Faculty of Engineering and Natural Sciences, Istinye University, 34396 Istanbul, Turkey