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The Landscape of Personalized Supplements

Harini Venkataraman, Ph.D., Analyst, and Jerrold Wang, Analyst
September 2, 2021

The supplement space continues to attract industry attention, as consumers are increasingly interested in their health and wellness in light of the COVID-19 pandemic and preventative measures. The dietary supplement industry is quite broad and includes vitamins, minerals, botanicals, amino acids, prebiotics, and probiotics. Vitamins and minerals make up the largest proportion of the industry, followed by probiotics; however, the industry is evolving, and in 2020, the intersection of personalization and direct-to-consumer (D2C) sales channels witnessed a boom. The timeline below provides an overview of recent activity by big players like Nestlé and Bayer. The conclusion is that majors are looking to improve their market presence through acquisitions as they look to expand their offerings related to supplements for consumer health and wellness.

Timeline providing an overview of recent activity by big players in the supplements industry

Despite this momentum indicating fertile opportunity, the supplements space is crowded, and more recent startups are marketing personalized solutions based on different types of data input rather than novel clinically backed ingredients. This reality necessitates a careful approach when engaging in this space, and you will need to clearly understand the data applied to inform personalization and product specificity. In this insight, we break down the data sources included in offerings claiming some degree of personalization as well as the types of supplements offered by these developers, associated challenges, and key observations from our analysis.

The five specificity levels

Companies provide personalized supplement solutions based on consumer data at five specificity levels, including physical traits and lifestyle decisions, genetics, biological biomarkers, digital biomarkers, and the microbiome. Each level is different in terms of the data collected, data collection methods, and insight derived about consumers.

  1. Physical traits and lifestyle

    Physical characteristics are well-demarcated, with examples including age, gender, weight, and height, whereas lifestyle preferences are explored through questionnaires and include daily sports activities, exercise level, and healthcare goals. The data collection is based on consumers’ input through smartphone apps or online questionnaires. Data at this specificity level can cover a very broad range of characteristics and activities and in some cases allow consumers to provide detailed information about their personalized demands. The metrics suffer from inaccuracy related to the nature of consumer-reported data. Well-evaluated surveys are necessary to improve accuracy.

    Most of the companies that Lux interacts with collect physical traits and lifestyle data because the data collection methods (smartphone app and online questionnaire) are widely available and easily passed on to and collected from consumers. Data at this specificity level is often not the sole source of information available and is instead combined with that of other levels, such as biological and digital biomarkers, to help companies establish more comprehensive consumer understanding. Persona and Care/of are among the few players that solely rely on physical traits and lifestyle data.

  2. Genomics

    Genomic data allows players to correlate genetic markers with complex nutrition, wellness, and health traits to identify genetic risk factors. For example, genetic markers may be strongly correlated with food allergies or the specific nutrition requirements of certain consumers, helping companies to come up with risk-tailored supplement solutions. The genomic information comes from the sequencing of specific regions of DNA. That DNA is typically isolated from a blood sample or cheek swab and sequenced by a third party.

    Data at this specificity level has the unique ability to shed light on consumers’ intrinsic health risk characteristics, but it has two major issues. First, given the multitude of factors that can impact gene-trait relationships, cause and effect recommendations are not typical. Second, personalized supplement companies often have a difficult time convincing consumers to consider any form of invasive sampling for genomic testing, especially when the state of nutrition is also dependent on the local environment and behavioral changes, and the cost of genomic testing and analysis from companies remains high (>$100 per sample).

    A small group of companies utilizes genomic data. Viome relies heavily on genomic data for its recommendations but may need to move beyond personalized supplements to disease prevention and management to achieve scaled growth. Alternatively, Mixfit only focuses on personalized supplements, but genomic data is only one of the optional data streams for consumer analysis.

  3. Biological biomarkers

    Data at this specificity level typically includes the conventional biomarker information collected from biological samples that are analyzed in a lab, such as blood, saliva, and urine. Compared with physical traits and lifestyle data, biological biomarker data is much more accurate and compatible with data analytics tools due to more well-recognized connections demonstrated by academic and medical literature. Compared to genomic data, biological biomarker data focuses on consumers’ ongoing, changing health conditions. Similar to genomic data, biological biomarkers are not easy for personalized supplement companies to collect, given the consumer behaviors or integration with medical assessments needed to obtain the measurements.

    Among the five specificity levels, biological biomarker data is the second most collected type of data for personalized supplements, after physical traits and lifestyle data. Companies collecting biological biomarker data usually combine it with other data streams, such as physical traits and lifestyle (like Hologram Sciences), genomics (like Caligenix), and microbiome (like Ixcela).

  4. Digital biomarkers

    Data at this specificity level is based on measurement on consumers, typically through noninvasive devices like wearable devices (mostly smartwatches) and smartphones, although the digital biomarker space is rapidly evolving. Compared with data at other specificity levels, digital biomarker data has unique benefits, as it offers a continuous noninvasive data source independent of location. Incumbent sensors in smartwatches can provide relatively accurate measurements for vital signs like heartbeat and blood oxygen saturation (SpO2), but measurements for vital signs like ECG and blood pressure still need improvement. Look for novel digital biomarkers to continue to emerge but go through a similar pace around developing confidence in their correlation to health and wellness traits and metrics.

    Digital biomarker data, especially wearable data, is an emerging specificity level among the five categories, with several companies like myAir and Mixfit incorporating wearable data into their data streams for consumer analysis. Both companies combine digital biomarkers with physical traits and lifestyle data and consider other data streams (like genomics, biological biomarker, and microbiome) as optional.

  5. Microbiome

    Data collected at this specificity level is based on an individual’s gut microbiome analysis. These tests are typically based on 16S rRNA analysis from an individual’s stool sample. Companies assign a gut health score based upon these microbial species and diversity (including bacteria, fungi, parasites, and viruses)-related analyses and recommend probiotics/prebiotics based on the microbiome signature. For instance, Sun Genomics uses whole-genome sequencing (WGS) to obtain species-level specificity from gut microbiome analysis. It recently raised Series A funding to scale its direct-to-consumer (D2C) platform providing personalized probiotics.

    As this is a niche segment within personalized supplements, developers are looking to gradually incorporate microbiome analysis for personalized biotics, but most are in the early commercial stages. There is still a gap between correlating an individual microbiome’s signature to the direct impact of personalized biotics and demonstrating real health outcomes compared to perceived health outcomes.



After examining the companies that Lux has investigated at five specificity levels in the field of personalized supplements, we developed three key observations:

  1. Validation of product performance is the unmet need. Companies heavily emphasize product personalization based on data at the specificity levels, but the vast majority of players do not provide convincing methods of product performance validation. There are three possible reasons for this from the perspective of the five specificity levels. First, physical traits and genomics are not suitable for validating product performance because the former is not qualitative or accurate enough and the latter requires significant population-level sampling to demonstrate a genetic predisposition. Second, biological biomarkers and the microbiome are good indicators of product performance, but the data collection frequency is usually too low to provide timely validation that can rule out other influencing factors. For example, Loewi’s blood test takes place every three to six months, although this is likely more sampling than has occurred for the consumer previously. Third, wearable data has the potential to become an immediate indicator of product performance, but companies like myAir and Mixfit still need to improve their metric validation methods by better defining their product performance and disclosing more information to rule out placebo effects.

  2. Low cost and simple data collection methods are currently the keys to driving adoption. Due to a lack of product performance validation, consumers can only see unproven and unquantified benefits, making it difficult for them to justify the costs (for supplements and biological biomarker testing kits) and commitment (behavioral changes like finger pricks) associated with product usage. As a result, if companies are not able to make breakthroughs in product performance validation, the products that will more easily obtain new users will be those with a low cost and simple data collection methods like physical trait and lifestyle data collection through smartphones and vital signs measurement via smartwatches. Cost and convenience are the current drivers in the personalized supplements space. For instance, Sun Genomics currently offers its personalized probiotics and testing at a nearly 50% discount from $179 to $99 per month.

  3. Companies with a broad spectrum of supplement offerings can better serve consumer needs. Compared to companies with limited supplement offerings, those with a wide range of supplement offerings have a stronger ability to customize solutions to better serve consumers’ diversified nutrition demands. Companies with a broad spectrum of supplement offerings are either those owning an established product portfolio (like Persona and Care/of) or those using an open product ecosystem to supply third parties’ supplements (like Mixfit). We expect that new entries into the personalized supplements space are more likely to follow the track of Mixfit, given the long process and high capex needed to launch a broad supplement portfolio. Among the most likely entries is LifeNome, which plans to add third-party nutrition supplements to its 9Moons precision nutrition pregnancy platform in Q4 2021.


Clients interested in personalized supplements should expect companies with low costs and simple data collection methods to have low friction for user conversion, at least in the near term. Clients willing to identify winners in the long term should actively seek developers that can move the needle on product performance validation and that can serve consumers’ diversified demands through an established supplement portfolio or an open supplement ecosystem.

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