Population studies can also take advantage of DBS technologies both for remote sampling with self-collection of blood and longitudinal studies. One of the main applications of DBS in population studies is newborn screening, which is nowadays used as a screening approach on all newborns for diagnosis of specific congenital disorders and conditions whose late diagnosis could result in the onset of irreversible symptoms [70]. NBS was first introduced in Europe in the 1960s for the screening of phenylketonuria, a metabolic disorder that affects the body’s ability to process amino acid phenylalanine [71]. Over the last decades, the development and the introduction of tandem mass spectrometry enlarged the panel of screened diseases (or conditions) that can be analyzed [72], making possible the screening for 40–50 conditions using a single blood spot [73]. In most western countries, a combined legislative scheme provided a nationwide NBS for over 40 disorders [74]. The screening is usually performed using a triple quadrupole mass spectrometer with a targeted metabolomics approach able to quantify amino acids and acylcarnitines [75].

Numerous studies conducted in recent years proposed new potential biomarkers for newborns’ diseases. The work of Mak et al. allowed the identification of a metabolic panel of 121 metabolites using DBS collected from newborns with an untargeted metabolomics approach. This panel could be used in second-tier assays to reduce the number of false positives for four metabolic diseases, glutaric acidemia type I (GA1), methylmalonic acidemia (MMA), ornithine transcarbamylase deficiency (OTCD), and very long-chain acyl-CoA dehydrogenase deficiency (VLCADD) [76]. Moreover, Brown and colleagues used tandem mass-spectrometry to identify short-chain carnitine and ornithine as potential biomarkers in newborns of succinic semialdehyde dehydrogenase deficiency (SSADHD) [77], a rare genetic disease associated with tissue accumulation of γ-aminobutyric acid and γ-hydroxybutyric acid neuromodulators, and tissue depletion of glutamine and glutamic acid [78].

In a very recent study, newborn DBS metabolomics has been used to explore the correlation between prenatal per- and polyfluoroalkyl (PFAS) exposure and gestational age at birth outcomes, finding that maternal serum PFAS levels during early to middle pregnancy, are associated with early birth prior to full-term among African American pregnant people and their newborns [79].

Another potential application of DBS is represented by disease surveillance, which is a valuable tool in epidemiology and public health. In fact, it is critical to monitor and control the spread of diseases, respond to outbreaks and inform healthcare interventions in specific populations, especially in resource-constrained or remote areas with difficult access to hospitals. Templer and colleagues indicate a new DBS device for blood collection with increased sensitivity for real-time PCR analyses [80]. This could lead to lower HIV-related morbidity and death as well as early detection and treatment of newborns infected with the virus [80].

Another field that might be interested in using microsampling and self-sampling strategies is nutritional metabolomics, which has the goal of correlating dietary patterns with health.

Petrick et al. investigated the impact of early life exposure on pediatric acute lymphoblastic leukemia (ALL) by conducting an untargeted metabolomics study. Neonatal DBS were collected within days from birth and analyzed via liquid chromatography high-resolution mass spectrometry (LC-HRMS). Since the age at diagnosis is an influencing factor, patients were grouped by early (1–5 years) and late (6–14 years) diagnosis. Results showed that levels of linoleic acid and linolenic acid were higher in the late-diagnosis group. Moreover, these two fatty acids were found in greater concentrations in children who were fed formula instead of breast milk, suggesting that nutrition in the early stages of life may be correlated with risks of ALL [82].

In the work of Pfluger and colleagues, DBS devices were used to find novel nutrition and metabolic indicators during infant weaning in Malian infants from 6 to 12 months supplied with heat-stabilized rice bran. The untargeted metabolomics analysis pointed out that heat-stabilized rice bran represented a great source of nutrients during weaning especially in low- and middle-income regions where nutritional deficiencies and nutrient deficiency and food scarcity are frequent. The supplementation leads to an increased concentration of several metabolites which are involved in multiple metabolic processes, including antioxidant defenses (reduced glutathione, glutamate and glycine), lipid profiles (short and medium-chain fatty acids), and neuroactive pathways (glutamic and aspartic acid, glycine, and asparagine) [83].

McNairn et al. investigated food intake by postprandial DBS collection in eight healthy volunteers. The aim of the study was to distinguish a high-fat, high-protein meat (HFPM) diet from a high-carbohydrate vegan (HCV) diet using metabolomics analysis. Blood samples were taken 3 h after breakfast and after lunch. In both postprandial DBS, higher levels of acylcarnitines, creatine, cis-trans-hydroxyproline and triacylglycerols were found in the HFPM diet. On the other hand, the HCV diet led to higher sorbitol concentrations. In summary, the two diets resulted in two significantly different metabolomics profiles in DBS. This study demonstrates that dietary metabolomics in DBS was able to distinguish the HFPM and HCV diets and can be an effective approach to monitoring food intake. It may be a useful tool to provide a more objective measure of food intake and enable a complementary alternative to conventional dietary assessment procedures [84].

Microsampling approaches have the potential to be integrated at various stages of drug development studies. Even though few studies have been published on this topic, microsampling has been probably used during metabolomics studies by the pharmaceutical industries in phase 1 and phase 2 of drug development, as evidenced by data compiled from responses received from 39 pharmaceutical companies and contract research organizations [85]. A substantial number of respondents have submitted microsampling data, both from nonclinical and clinical studies, to various regulatory agencies [85]. Notably, microsampling finds more routine adoption in nonclinical studies compared to clinical studies. Within nonclinical investigations, microsampling approaches are increasingly prevalent in discovery phase projects as opposed to later-phase non-GLP and GLP studies [85].

Therapeutic drug monitoring (TDM) is a specialized clinical practice that consists of the measurement of the concentration in plasma or blood of a wide range of drug classes, such as antiepileptics, antimicrobial agents and immunosuppressants [86,87,88,89,90,91]. Most of the studies are focused on the quantification of the drugs in blood or serum using DBS [92], but in recent years, pharmacometabolomics has emerged, aiming at the evaluation of the metabolic fingerprints following drug administration [93]. Even with some limitations, i.e., the standard protocol for sample preparation and storage, environment influence, and ethnicity, this type of study could be useful to evaluate drug toxicity and pharmacokinetics, but also predict the pharmaceutical answer and its pharmacodynamics [94].

One of the main advantages of microsampling techniques is that they can be easily extended in studies where animal models are included [95,96,97]. The use of this technique represents an important and useful tool for blood sampling during the study, and in accordance with two out of three of the 3Rs, reduction and refinement. In fact, microsampling devices allow multiple sampling, without pain and stress in the animal during the collection, and consequently permit the reduction in the number of animals necessary for the study [96]. For instance, Volani et al. demonstrated that the integration of VAMS technology and MS-based metabolomics allows one to find specific longitudinal metabolic variations after iron supplementation [95].

In the realm of sports, the integration of metabolomic blood analyses and microsampling provides a unique avenue to delve into the molecular repercussions of physical activity. In this context, multiple sampling is often required and often blood collection is performed during competition of training in the field. Therefore, microsampling simplifies the logistics of sample collection and facilitates the possibility of using omics analysis in evaluating dynamic changes in the metabolome during physical activity.

Puigarnau et al. delved into the impact of high-intensity activities, such as trail running, on blood metabolites. Employing volumetric absorptive microsampling devices (Mitra^® Clam-shell, Neoteryx, Trajan Scientific and Medical, Melbourne, VIC, Australia), capillary blood was collected from thirty-three participants before and after the race. Stratifying participants based on training levels—low, moderate, and high—revealed significant variations in pre and post-race concentrations of metabolites like taurine and acetyl-carnitine. Notably, highly trained athletes exhibited minimal alterations compared to their less-trained counterparts [98].

Cendali et al. conducted a parallel study, focusing on running exercise. Blood samples from twelve volunteers were collected before, immediately after, and 24 h post-activity. Mass spectrometry analysis highlighted the DBS approach’s potential for longitudinal metabolic profiling, revealing sustained changes in nitrogen homeostasis-related compounds. Gender-specific variations were unraveled, potentially linked to the chromosome X location of the glucose 6-phosphate dehydrogenase (G6DP) gene, with consequential impacts on lactate and pyruvate levels [99].

Nix et al. pursued a targeted metabolomics analysis, utilizing microsampling with hemaPEN^® devices (Trajan Scientific and Medical, Melbourne, VIC, Australia). Twenty participants underwent a 400 m warm-up followed by acute running exercise, monitored at multiple time-points. Lactic acid emerged as a pivotal metabolite, exhibiting a significant surge during exercise, reverting to baseline levels half an hour post-exercise, aligning with the existing literature trends [18].

Nemkov et al. characterized molecular exertion profiles in elite athletes during a World Tour cycling competition. Untargeted metabolomics and lipidomics analysis were performed on blood samples from 28 elite male athletes using VAMS devices. The blood samples were collected before and after a graded exercise test and before and after a long aerobic training. Furthermore, five cyclists were selected for additional analysis during a seven-stage elite World Tour race. The graded exercise test highlighted a significant accumulation of lactate, succinate, free fatty acids and acylcarnitines. During the long aerobic exercise session, a significant increase in fatty acids and acylcarnitines was observed without major changes in lactate and succinate. A similar result was obtained in samples from sprinting and climbing stages of the World Tour, along with an increased fatty acid oxidation capacity associated with competitive performance. This study provides a useful insight into the metabolomic and lipidomic changes in blood during elite competition. It also demonstrates the benefits of microsampling devices that allow blood sampling in training and in competition [100].

Bassini et al. investigated the inflammatory response to understand exercise-induced changes by combining sportomics with DBS and multiplex mass spectrometry. Since exercise is known to lead to changes in acute-phase proteins that may be associated with overtraining, a panel of 11 blood proteins in 687 samples was analyzed. Blood samples were collected using Whatman 903 Protein Saver cards from 97 elite and Olympic-level athletes (men and women) from 16 sports. The results show that five acute phase proteins were highly correlated and that this correlation varied among the 16 sports analyzed. For example, CRP-SAA1 (C-reactive protein-serum amyloid A1) and CRP-LBP (C-reactive protein-lipopolysaccharide-binding protein) were found to be highly correlated in acute inflammation in response to exercise. In addition, a correlation was suggested between the reduction in TLR4 (Toll-like receptor 4) and the protective effect of exercise in heart diseases. A total of 1500 protein–protein interactions were highlighted in this study and 30 of the 44 core proteins were associated with immune system processing [101].

The emerging field of multi-omics refers to the integrative analysis of various biological data sets, including genomics, transcriptomics, proteomics, metabolomics, and epigenomics. Using several layers of molecular data at once, this method seeks to give a thorough and all-encompassing knowledge of complex biological systems and opens a new horizon for precision medicine [102]. Such an integrative approach has been used on entire blood and plasma, achieving promising results in the understanding of complex biological processes, leading to more precise and personalized diagnostics as well as targeted therapeutic interventions. As such, multi-omics approaches allowed us to improve the characterization of COVID-19 infection, identifying both predictive biomarkers of severity [103] and anti-inflammatory response [104]. A similar strategy has been applied to investigate possible biomarkers of neurological diseases [105,106,107] and metabolic disorders [108,109,110]. However, the initial stage of sample collection and handling still represents a bottleneck for large-cohort and long-term studies [111]. In this context, a microsampling approach could play a key role in minimizing the invasiveness of sample collection and the consequent stress for the subjects under investigation. Interestingly, Shen et al. [111] performed two case studies involving participants in their native environments. The first case study aimed to investigate the effects of drinking a complex mixture on metabolic profiles at different timepoints. The study included 32 participants who were mailed the microsampling kits and the same shake. The subjects were instructed to collect the first sample prior to consuming the shake, as well as four additional samples at 30, 60, 120, and 240 min after consumption. Microsamples were then used to extract metabolomics, lipidomics, and cytokines/hormones profiles. The multi-omics results revealed different alterations in time, reporting significant alterations among different classes of analytes at different time points. Specifically, the changes in molecules were classified into three major clusters across five time points. The first cluster consisted primarily of amino acids and cytokines, which increased rapidly with a peak at 60 min and then decreased. The second cluster contained mostly lipids increasing up to 60–120 min before decreasing. The third cluster included mostly acylcarnitines, decreasing in response to shake consumption and recovering after 240 min. Additionally, the t-distributed stochastic neighbor embedding (tSNE) plot of the multi-omic data revealed that each participant had a unique molecular profile that could be further classified into two main groups depending on their ability to respond to the shake. Furthermore, the authors investigated the kinetics of each class of metabolite, defining six metabolic scores: carbohydrate, lipid, free fatty acids (FFAs), protein, and insulin secretion cytokines. Thus, the multi-omics data from microsamples revealed significant heterogeneity in the biochemical responses of each individual to a complex nutritional complex. In the future, such data could be linked to medical phenotypes and used to provide personalized nutrition management. The second case study proposed by Shen et al. focused on a 24/7 personalized whole physiology profiling of a single individual using wearable and multi-omics data. Over the course of 7 days, a single participant collected blood microsamples every 1–2 h during waking hours, collecting a total of 98 microsamples. On the same days, a smartwatch was used to record heart rate (HR) and step count, along with a continuous glucose monitor (CGM) and food logging was also recorded. In-depth multi-omics profiling, including untargeted proteomics, untargeted metabolomics, targeted lipidomics and targeted cytokine, hormone, total protein, and cortisol assays, was performed on the 98 microsamples. A total of 2213 analytes were annotated among metabolites, lipids, and proteins. To examine if the measured metabolic changes in the individual could reflect real changes, the authors verified metabolic changes in a day with high carbohydrate consumption and in a day with low carbohydrate intake, confirming the validity of the test. It was also possible to monitor cortisol levels changes across the day, following not only circadian rhythms but also stress levels. The frequent sampling also allowed monitoring of inflammatory events, corresponding to an increase in cytokines occurring without symptoms. These data in particular could be of extreme importance for monitoring patients and for the early detection of disease. Correlating the multi-omics with the wearable data showed a correlation between specific molecular classes and short-term physiological changes. Overall, the results of this pilot study provide a valuable basis for further multi-omic investigations with longitudinal studies. In this context, microsampling brings important advantages when aiming at personalized medicine. The low amount of sample and the low invasiveness of the procedure make it ideal for high-frequency collection and longitudinal biomarkers [112]. Nonetheless, the investigation of other molecular levels and the investigation of large sample cohorts will certainly require improvements in data analysis and integration. Furthermore, future multi-omics studies must consider the limitations of traditional DBS, such as hematocrit volume and analyte stability, self-sampling, and hemolysis. Moreover, proteins, RNA, DNA, and/or glycans, might require specific extraction and storage conditions. However, these limitations may be overcome by technological advances in both DBS systems and optimization of the analytical and preanalytical aspects [113].