Classification essays rank the groups of objects according to a common standard. For example, popular inventions may be classified according to their significance to the humankind.
Classification is a convenient method of arranging data and simplifying complex notions.
When you select a topic, do not forget about the length of your paper. Choose the topic you will be able to cover in your essay, do not write about something global or general.
Consider these examples:
- Evaluate the best to worst methods of upbringing.
- Rate the films according to their influence on people.
- Classify careers according to the opportunities they offer.
You should point out the common classifying principle for the group you are writing about. It will become the thesis of your essay.
It is important for you to use clear method of classification in your essay, especially when you are dealing with subjective categories such as "quality" or "benefit". Make sure you explain what you mean by this term.
To organize a classification essay, the writer should:
- categorize each group.
- describe or define each category. List down the general characteristics and discuss them.
- provide enough illustrative examples. An example should be a typical representative of the group.
- point out similarities or differences of each category, using comparison-contrast techniques.
Diet is a contributing factor to the development of numerous diseases including obesity, cardiovascular disease, and type 2 diabetes. Increasingly, these complex metabolic diseases are also being associated with the gastrointestinal microbiota . The gastrointestinal microbiota, an ecosystem containing trillions of microorganisms, including bacteria, archaea, and fungi, is, in essence, a microbial organ that possesses more than 150 times more genes than the human genome . Importantly, these microbes possess genes that allow for the metabolism of foods that escape digestion by human alimentary enzymes, thereby aligning diet as an important modulator of the interrelationship between diet, the gastrointestinal microbiota, and host health.
Diet is a key modulator of the human gastrointestinal microbiota—habitual dietary patterns , rapid changes in dietary macronutrient composition , eating behaviors , and increased intake of different types of plant-based foods  have been linked to the composition of human gastrointestinal microbiota. To date, much of the research on specific diet components has focused on the impact of consumption of different dietary fibers and prebiotics on the human gastrointestinal microbiota [7,8,9,10]. These studies have revealed that the molecular composition, including the glycosidic linkages between monosaccharides, as well as physicochemical properties of fibers influence the microbial composition and production of bacterial metabolites including short-chain fatty acids . Preclinical research and in vitro studies have also demonstrated that dietary fats can differentially impact the gut microbiota and that certain microbes can utilize fatty acids as energy substrates [12,13,14,15]. Almonds are a nutrient-dense food that provide a good source of dietary fiber (5 g per 42 g almonds) as well as unsaturated fatty acids (20 g per 42 g almonds).
Dietary consumption of nuts is associated with reduced risk of diseases including, obesity, cardiovascular disease, type 2 diabetes, and cancer [16,17]. Furthermore, epidemiological evidence indicates that individuals who regularly consume tree nuts exhibit lower waist circumference and superior metabolic profiles . Interestingly, clinical trials with almonds have demonstrated that food form (e.g., whole, chopped, ground) differentially impacts metabolizable energy due to incomplete absorption of macronutrients in the gastrointestinal tract . As nutrients that are not absorbed in the proximal gastrointestinal tract are subject to microbial fermentation, dietary consumption of nuts in different forms stand to influence the composition of the gastrointestinal microbiota. Indeed, the health-related effects of nut consumption may not only be related to their metabolizable energy, but also their impact on the gastrointestinal microbiota. One hypothesized connection between the metabolic improvements shown with almond consumption is increased butyrate production by gastrointestinal microbes. In vitro, microbial fermentation of finely ground almonds has been shown to increase butyrate concentrations , and preclinical studies have demonstrated that increased butyrate concentrations are linked to reduced gut inflammation , improved gut barrier function , and glucose tolerance .
Previously, we conducted a controlled-feeding, randomized, cross-over trial in healthy adult men and women to determine the metabolizable energy of different forms of almonds, including whole, natural almonds; whole, roasted almonds; roasted, chopped almonds; and almond butter and reported that the metabolizable energy of whole natural almonds, whole roasted almonds, and chopped almonds was up to 25% lower than predicted by calculation using the Atwater values . Indeed, the process of roasting and chopping almonds reduced their hardness and particle size, increasing the digestion and absorption of lipids in the small intestine, which resulted in metabolizable energy values of 4.42 kcal/g for whole natural, 4.86 kcal/g for whole roasted, and 5.04 kcal/g for roasted chopped almonds. The addition of pureeing the roasted almonds into butter ablated this inhibition of digestion and absorption revealing that the calculated and measured metabolizable energy of almond butter was not different, 6.68 kcal/g and 6.53 kcal/g, respectively. Thus, the degree of almond processing (e.g., roasting, chopping, and grinding) affected the nutrients delivered to the large intestine and excreted in the feces. In this follow-up report, we aimed to assess our secondary outcomes of the study and determine the effect of almond consumption and processing on the gastrointestinal microbiota by subjecting the same fecal samples from the clinical trial on metabolizable energy of almonds to high-throughput sequencing. The objectives of the current study were to determine how almond consumption and almond form (e.g., whole, whole roasted, chopped, and butter) differentially impact the bacteria, archaea, and fungi in the human gastrointestinal tract. We hypothesized that consumption of almonds and food form would impact the human fecal microbiota compared to control.
2.1. Experimental Design, Treatments, and Subjects
This report is a follow-up to a previously conducted clinical trial (NCT02034383). Details of the design and results of the primary study, which assessed the metabolizable energy of almonds, were previously reported . Herein, secondary outcomes related to almond consumption on the gastrointestinal microbiota were assessed. Briefly, this was a controlled-feeding, randomized, cross-over study with five three-week diet periods, each diet period was separated by a one-week non-controlled diet break (wash-out) period. The controlled diets, fed at weight maintenance, consisted of a base diet designed to represent the typical American diet, with a macronutrient composition of 53% of kcals from carbohydrates, 32% of kcals from fat, and 15% of kcals from protein, and 0 g/day of almonds. All foods were identical between the control and almond diets, except for the almonds. During the four almond diet periods of the study, the controlled diet was scaled down to allow for the isocaloric inclusion of 42 g/day of varying forms of almonds: whole, natural almonds; whole, dry roasted almonds; chopped, dry roasted almonds; and dry roasted, almond butter.
Study participants were recruited in the Washington, DC metropolitan area surrounding the Beltsville Human Nutrition Research Center (BHNRC) in Beltsville, MD, USA. All subjects gave their informed consent for inclusion before participating in the study. The study was conducted in accordance with the Declaration of Helsinki, and the protocol and informed consent document were reviewed and approved by the Medstar Health Research Institute (Protocol #2013-136). Participants were excluded if they were <25 or >75 years of age, had a body mass index (BMI) <20 or >38 kg/m2, blood pressure ≥160/100 mm Hg, fasting blood glucose ≥126 mg/dL, fasting total blood cholesterol ≥280 mg/dL, and fasting triglycerides ≥300 mg/dL. Participants were excluded if they had any of the following diseases or conditions: allergy to almonds or other nuts, gastrointestinal disease, malabsorptive syndromes, liver disease, pancreatic disease, kidney disease, gout, hyperthyroidism, untreated or unstable hypothyroidism, certain cancers, or type II diabetes; history of bariatric or certain other surgeries related to weight control; history of eating disorders or other dietary patterns which are not consistent with the dietary intervention (e.g., vegetarians, very low fat diets, high protein diets); greater than 10% change in body weight within the last 12 months; pregnant women or women who plan on becoming pregnant during the study period; women who had given birth within the past 12 months; and tobacco users. A random number generator was utilized for study participant randomization following the creation of a randomization scheme that stratified the 18 participants (10 males; eight females) by sex and BMI. Each participant was assigned to a treatment sequence that consisted of each of the five diet periods.
2.2. Sample Collection and Analysis
Fecal samples were collected at the end of each of the five diet treatment periods (e.g., control, whole almonds, whole roasted almonds, roasted chopped almonds, and almond butter) for microbiota analyses. Following fecal collection and homogenization, samples were stored at −80 °C at the BHNRC prior to overnight shipment on dry ice to the University of Illinois and subsequent storage of samples at −80 °C until analysis. Fecal DNA was extracted according to manufacturer’s guidelines using the MoBio Powerlyzer Powersoil Kit (MoBIO Laboratories Inc., Carlsbad, CA, USA). After extraction, sequencing was performed at the W. M. Keck Center for Biotechnology at the University of Illinois as previously described . Briefly, bacterial (V4 region of 16S rRNA; 515F/806R: 5′-GTGCCAGCMGCCGCGGTAA, 5′-GGACTACHVGGGTWTCTAAT), archaeal (ArchaeaF/ArchaeaR: 5′-GYGCASCAGKCGMGAAW, 5′-GGACTACVSGGGTATCTAAT) and fungal (ITS1F/ITS4R: 5′-TTCGTAGGTGAACCTGCGG, 5′-TCCTCCGCTTATTGATATGC) genes were amplified using a Fluidigm access array (Fluidigm, South San Francisco, CA, USA). Following amplification, a fragment analyzer (Advanced Analytics, Ames, IA, USA) was utilized to check the quality of the amplicon pool and confirm amplicon region and size. Next, a DNA pool was generated by combining equimolar amounts of the amplicon from each sample. Then, amplicon pools were size-selected on a 2% agarose E-gel (Life Technologics, Grand Island, NY, USA) to remove low molecular weight DNA fragments. A Qiagen gel purification kit (Qiagen, Valencia, CA, USA) was used to extract the amplicon pools from the gel and remove impurities. Then, size-selected, cleaned amplicon pools were analyzed using an Agilent Bioanalyzer (Advanced Analytics, Ames, IA, USA) to confirm size selection and quality. Lastly, the amplicon pool was sequenced using an Illumina MiSeq (Illumina Inc., San Diego, CA, USA) with version 3 chemistry.
Sequence data derived from the sequencing process were trimmed using the the open access FASTX-toolkit (v 0.0.14), and demultiplexed and analyzed with the open access bioinformatics software QIIME 1.8.0. Briefly, bacterial and archaeal sequences were clustered into operational taxonomic units (OTUs) using the default UCLUST closed-reference OTU picking algorithm (pick_closed_reference_otus.py) against the curated Greengenes 13_8 reference OTU database (97% similarity threshold), which discards sequences that do not match to the reference database [25,26]. Fungal OTUs were generated using the open reference OTU picking algorithm (pick_open_reference_otus.py) against the UNITE OTUs ITS 12_11 reference database . Singletons (OTUs observed fewer than two times) and OTUs that represented less than 0.005% of the total observations were removed  before rarefying the bacterial sequences to an even sampling depth of 8227 sequences per sample to assess alpha (observed OTUs and phylogenetic diversity) and beta diversity (weighted and unweighted Unifrac distances) measures as well as the impact of treatment on OTU abundances that that represented ≥0.5% of the total sequences.
Sequence percentages were analyzed using the mixed models procedure of SAS (version 9.4; SAS Institute, Inc., Cary, NC, USA) with treatment as a fixed effect and participant and period as random effects. Post hoc Dunnets adjustments were used to control for multiple comparisons. To determine if the presence or absence of almonds affected the fecal microbiota, contrast statements were utilized to compare almond (pooled whole, whole roasted, chopped, and butter) to the control diet period. To assess the effect of processing on the microbiota, each almond group (e.g., whole, whole roasted, chopped, or butter) was contrasted against control. The UNIVARIATE procedure and Shapiro–Wilk statistic were used to test for data normality, and log transformations were utilized as needed. The Wilcoxon test was used for fungal count analyses. A probability of p ≤ 0.05 was accepted as statistically significant, and p ≤ 0.10 was considered a trend.
All 18 participants, 10 men and eight women, that were randomized completed the study. Participants had a mean BMI of 29.7 + 4.4 kg/m2 and an age of 56.7 + 10.2 years (Table 1).
Following demultiplexing and quality filtering, the mean and standard deviation of bacterial OTUs identified using closed reference OTU picking was 33,194 ± 9686, with a minimum of 8227 counts per sample. As such, all samples were rarefied to an even sequencing depth of 8227 for subsequent analyses. At the phyla level, more than 90% of bacterial sequences were dominated by Firmicutes (64.2%) and Bacteroidetes (27.5%), with smaller proportions of Actinobacteria (3.8%), Proteobacteria (2.0%) and Verrucomicrobia (1.6%). Forty bacterial families were identified. The top seven families, Ruminococcaceae (28.9%), Lachnospiraceae (20.4%), Bacteroidaceae (21.0%), an undefined family in the Clostridales order (8.1%), Veillonellacaea (3.3%), Bifodobacteriaceae (2.5%), and Verrucomicrobiaceae (1.6%), comprised approximately 90% of the sequences. Seventy-eight genera were classified with 21 representing greater than 90% of total sequences and 34 representing <1%; the top 10 genera, Bacteroides, unclassified Ruminococcaceae, unclassified Clostridiales, an unclassified Lachnospiraceae, Coprococcus, Faecalibacterium, Ruminococcus, Bifidobacterium, Blautia, and an unclassified genera in the Rikenellacaeae family represented approximately 77% of total sequences classified at the genera level.
Archaeal sequences accounted for approximated 8.4% of total sequences. Following demultiplexing and quality filtering, the mean and standard deviation of archaeal OTUs identified using closed reference OTU picking was 3081 ± 5289, with a maximum of 26,030 counts per sample and a minimum of zero counts per sample. Treatment did not impact archaea relative abundances. Fungi accounted for approximately 0.1% of total sequences; the mean and standard deviation was 256 + 560 OTUs per sample. The minimum was one OTU/sample (12 individuals), and the maximum was 2410 sequences. Study participants had more (p < 0.05) fungal OTUs when consuming the whole almonds treatment compared to the other treatment groups.
To determine if the presence or absence of almonds in the diet affected the fecal microbiota, contrast statements were utilized to compare almond (pooled whole, whole roasted, chopped, and butter) to the control diet period. The data revealed that almond consumption decreased the relative abundance of the Actinobacteria phylum, as well as Bifidobacterium and Parabacteroides (p < 0.05). The relative abundance of Lachnospira, Roseburia, Clostridium, and Dialister (p ≤ 0.05) increased when the almonds were consumed compared to the control (Table 2). To assess the effect that processing has on the microbiota, each almond group (e.g., whole, whole roasted, chopped, or butter) was contrasted against the control. These results revealed that processing differentially impacted the microbiota. Compared to the control, chopped almonds increased the relative abundance of Lachnospira, Roseburia, and Oscillospira (p < 0.05); whole roasted almonds increased (p = 0.03) the relative abundance of Lachnospira; and whole raw almonds increased (p = 0.007) the relative abundance of Dialister (Table 3). There were no differences between control and almond butter. Almond consumption did not affect bacterial alpha diversity or beta diversity measures (Supplemental Figures S1–S4).